CV2019.md

AI-News 2019

• Semantation(new)
• Re-Identification
• Classification
• Graph Neural Networks
• Super-Resolution
• Registration
• Normalization
• Survey
• Dataset
• Conference and Journal

Segmentation

• Semantic Segmentation
• 2D Medical Segmentation
• 3D Medical Segmentation
• Instance Segmentation
• Panoptic Segmentation
• Video Segmentation

Semantic Segmentation

2020

• AAAI
  • Segmenting Medical MRI via Recurrent Decoding Cell[Code]
  • F3Net: Fusion, Feedback and Focus for Salient Object Detection[Code]
  • An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation
  • JSNet: Joint Instance and Semantic Segmentation of 3D Point CloudsCode
• ICLR
  • FasterSeg: Searching for Faster Real-time Semantic Segmentation [Code]

2019

• Nature Machine Intelligence
  • Clinically applicable deep learning framework for organs at risk delineation in CT images[Code]
  • An integrated iterative annotation technique for easing neural network training in medical image analysis[Code]
  • Continual learning of context-dependent processing in neural networks
  • Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks[Code]
  • Deep learning and alternative learning strategies for retrospective real-world clinical data[Code]
• NEW
  • PointRend: Image Segmentation as Rendering[Kaiming He]
  • Momentum Contrast for Unsupervised Visual Representation Learning[Kaiming He]
  • Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research[Code]
  • Reinventing 2D Convolutions for 3D Medical Images[Code]
  • A Multigrid Method for Efficiently Training Video Models[Kaiming He]
• ICCV2019
  • ShelfNet for Fast Semantic Segmentation[Code]
  • Recurrent U-Net for Resource-Constrained Segmentation
  • Eye Semantic Segmentation with a Lightweight ModelCode[Workshop]
  • On the Efficacy of Knowledge Distillation
  • DeepGCNs: Making GCNs Go as Deep as CNNs
  • SegSort: Segmentation by Discriminative Sorting of Segments[Code]
  • Domain Adaptation for Semantic Segmentation with Maximum Squares Loss
  • ACFNet: Attentional Class Feature Network for Semantic Segmentation
  • Asymmetric Non-local Neural Networks for Semantic Segmentation[Code]
  • Gated-SCNN: Gated Shape CNNs for Semantic Segmentation[Project]
  • CCNet: Criss-Cross Attention for Semantic Segmentation[Pytorch]
  • SPGNet: Semantic Prediction Guidance for Scene Parsing
  • Expectation-Maximization Attention Networks for Semantic Segmentation(31 Jul)[Code]
  • Boundary-Aware Feature Propagation for Scene Segmentation
  • Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
  • Explicit Shape Encoding for Real-Time Instance Segmentation[Code]
  • Joint Learning of Saliency Detection and Weakly Supervised Semantic Segmentation
  • Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation
  • Semantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation
  • Incremental Class Discovery for Semantic Segmentation with RGBD Sensing
  • Similarity-Preserving Knowledge Distillation
  • Orientation-aware Semantic Segmentation on Icosahedron Spheres
  • Incremental Learning Techniques for Semantic Segmentation
  • Cascaded Context Pyramid for Full-Resolution 3D Semantic Scene Completion
  • Learning Lightweight Lane Detection CNNs by Self Attention Distillation[tensorflow]
  • SqueezeNAS: Fast neural architecture search for faster semantic segmentation[[ICCVW]]
  • GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing
  • Interpolated Convolutional Networks for 3D Point Cloud Understanding
  • PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment
  • Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization
  • Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition[Code]
  • Attention on Attention for Image Captioning[Code]
  • A Fast and Accurate One-Stage Approach to Visual Grounding
  • Context-Aware Emotion Recognition Networks[Code]
  • Learning Filter Basis for Convolutional Neural Network Compression[Pytorch]
  • Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels
  • Deep Camera: A Fully Convolutional Neural Network for Image Signal Processing[[workshop]]
  • Dual Attention MobDenseNet(DAMDNet) for Robust 3D Face AlignmentCode[[workshop]]
  • Adversarial Learning with Margin-based Triplet Embedding RegularizationCode
  • Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the LoopCode
  • RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking[Code][workshop]
  • Mask-Guided Attention Network for Occluded Pedestrian Detection[Code]
  • Guided Image-to-Image Translation with Bi-Directional Feature Transformation[Code]
  • Seeing What a GAN Cannot Generate[Code]
• NIPS 2019
  • Object landmark discovery through unsupervised adaptation[Code]
  • Implicit Semantic Data Augmentation for Deep Networks[Pytorch]
  • Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations[code]
  • FireNet: Real-time Segmentation of Fire Perimeter from Aerial Video[Workshop]
• BMVC2019
  • MixNet: Mixed Depthwise Convolutional KernelsCode
  • DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation[Pytorch]
  • Dual Graph Convolutional Network for Semantic Segmentation
  • Global Aggregation then Local Distribution in Fully Convolutional NetworksCode
  • Feature Pyramid Encoding Network for Real-time Semantic Segmentation
  • Accurate and Compact Convolutional Neural Networks with Trained Binarization[[oral]]
  • Referring Expression Object Segmentation with Caption-Aware Consistency[Pytorch]
• CVPR2019
  • Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search[Code]
  • Searching for A Robust Neural Architecture in Four GPU Hours[Code][camera-ready version]
  • Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation[Tensorflow][Pytorch]
  • ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network[Code]
  • DFANet：Deep Feature Aggregation for Real-Time Semantic Segmentation[Pytoch]
  • In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images(Mar 2019))[Pytroch]
  • Knowledge Adaptation for Efficient Semantic Segmentation(Mar 2019)
  • Dual Attention Network for Scene Segmentation(Sep 2018)[Pytorch]
  • Adaptive Pyramid Context Network for Semantic Segmentation
  • Co-Occurrent Features in Semantic Segmentation
  • Structured Knowledge Distillation for Semantic Segmentation(Mar 2019)
  • CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning
  • Semantic Correlation Promoted Shape-Variant Context for Segmentation
  • Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images[oral]
  • Bidirectional Learning for Domain Adaptation of Semantic Segmentation(Apr 2019)[Pytoch]
  • Seamless Scene Segmentation
  • Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation
  • Cross-Modal Self-Attention Network for Referring Image Segmentation(Apr 2019)
  • Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation[Pytorch]
  • FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference
  • A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
  • Large-scale interactive object segmentation with human annotators
  • Deep Modular Co-Attention Networks for Visual Question Answering
  • Enhancing Salient Object Segmentation Through Attention[CVPRW】
  • The Ethical Dilemma when (not) Setting up Cost-based Decision Rules in Semantic Segmentation
  • Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation
• AAAI 2019
  • Learning Fully Dense Neural Networks for Image Semantic Segmentation(May 2019)
• ICIP2019
  • Incorporating Luminance, Depth and Color Information by a Fusion-based Network for Semantic Segmentation[Code]
  • Implicit Background Estimation for Semantic Segmentation
  • LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation
  • What's There in The Dark[Keras]
  • Diversity in Fashion Recommendation using Semantic Parsing[pytorch]
• SPL
  • RFBNet: Deep Multimodal Networks with Residual Fusion Blocks for RGB-D Semantic Segmentation(29 Jun)
• SCI
  + Self-Supervised Model Adaptation for Multimodal Semantic Segmentation[Code][IJCV]
  + Fine-grained Action Segmentation using the Semi-Supervised Action GAN[ Pattern Recognition]
• WACV
  • CNN-based Semantic Segmentation using Level Set Loss
  • Shape Constrained Network for Eye Segmentation in the Wild[Code]
• ICCD
  • VNet: A Versatile Network for Efficient Real-Time Semantic Segmentation
• other
  • SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images[Code]
  • PixelRL: Fully Convolutional Network with Reinforcement Learning for Image Processing[Code]
  • Semantic Segmentation for Compound figures
  • LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation
  • Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation
  • RGPNet: A Real-Time General Purpose Semantic Segmentation
  • Deep Object Co-segmentation via Spatial-Semantic Network Modulation
  • RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds[Code]
  • Stagewise Knowledge Distillation
  • Location-aware Upsampling for Semantic SegmentationCode
  • Improving Semantic Segmentation via Dilated Affinity
  • LPRNet: Lightweight Deep Network by Low-rank Pointwise Residual Convolution
  • Attention Mechanism Enhanced Kernel Prediction Networks for Denoising of Burst Images[Pytorch]
  • Background Segmentation for Vehicle Re-Identification
  • Saliency Guided Self-attention Network for Weakly-supervised Semantic Segmentation
  • Unrestricted Adversarial Attacks for Semantic Segmentation
  • Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation
  • 3D Neighborhood Convolution: Learning Depth-Aware Features for RGB-D and RGB Semantic Segmentation
  • End-to-End Deep Convolutional Active Contours for Image Segmentation
  • IPC-Net: 3D point-cloud segmentation using deep inter-point convolutional layers
  • Point Attention Network for Semantic Segmentation of 3D Point Clouds
  • Object-Contextual Representations for Semantic Segmentation[Code]
  • A New Few-shot Segmentation Network Based on Class Representation
  • Object Segmentation using Pixel-wise Adversarial Loss
  • Graph-guided Architecture Search for Real-time Semantic Segmentation
  • Boosting Real-Time Driving Scene Parsing with Shared Semantics
  • Squeeze-and-Attention Networks for Semantic Segmentation
  • Semantic Segmentation of Panoramic Images Using a Synthetic Dataset
  • Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation
  • Revisiting CycleGAN for semi-supervised segmentation
  • LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net
  • Where Is My Mirror?
  • Don't ignore Dropout in Fully Convolutional Networks
  • See More Than Once -- Kernel-Sharing Atrous Convolution for Semantic Segmentation
  • Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach
  • Consensus Feature Network for Scene Parsing
  • Semi-Supervised Semantic Segmentation with High- and Low-level Consistency
  • See Clearer at Night: Towards Robust Nighttime Semantic Segmentation through Day-Night Image Conversion
  • Adaptative Inference Cost With Convolutional Neural Mixture Models
  • Dynamic Graph Message Passing Networks
  • PS^2-Net: A Locally and Globally Aware Network for Point-Based Semantic Segmentation
  • MoGA: Searching Beyond MobileNetV3
  • I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation
  • EdgeNet: Semantic Scene Completion from RGB-D images
  • ExtremeC3Net: Extreme Lightweight Portrait Segmentation Networks using Advanced C3-modules
  • Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes
  • DAR-Net: Dynamic Aggregation Network for Semantic Scene Segmentation
  • Dilated Point Convolutions: On the Receptive Field of Point Convolutions
  • ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks
  • Grid Saliency for Context Explanations of Semantic Segmentation
  • A Comparative Study of High-Recall Real-Time Semantic Segmentation Based on Swift Factorized Network
  • Semantic Deep Intermodal Feature Transfer: Transferring Feature Descriptors Between Imaging Modalities
  • Context-Integrated and Feature-Refined Network for Lightweight Urban Scene Parsing
  • Single Level Feature-to-Feature Forecasting with Deformable Convolutions
  • SDNet: Semantically Guided Depth Estimation Network
  • Self-supervised Domain Adaptation for Computer Vision TasksPytorch
  • Make Skeleton-based Action Recognition Model Smaller, Faster and Better
  • LYTNet: A Convolutional Neural Network for Real-Time Pedestrian Traffic Lights and Zebra Crossing Recognition for the Visually Impaired
  • RRNet: Repetition-Reduction Network for Energy Efficient Decoder of Depth Estimation
  • Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks
  • Adaptive Context Encoding Module for Semantic Segmentation
  • Mango Tree Net -- A fully convolutional network for semantic segmentation and individual crown detection of mango trees
  • Data Selection for training Semantic Segmentation CNNs with cross-dataset weak supervision
  • Efficient Segmentation: Learning Downsampling Near Semantic Boundaries
  • VarGNet: Variable Group Convolutional Neural Network for Efficient Embedded Computing
  • Gated-SCNN: Gated Shape CNNs for Semantic Segmentation(12 Jul)Code
  • SAN: Scale-Aware Network for Semantic Segmentation of High-Resolution Aerial Images
  • Slim-CNN: A Light-Weight CNN for Face Attribute Prediction
  • ELKPPNet: An Edge-aware Neural Network with Large Kernel Pyramid Pooling for Learning Discriminative Features in Semantic Segmentation
  • Hard Pixels Mining: Learning Using Privileged Information for Semantic Segmentation
  • ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation[Pytorch]
  • Recurrent U-Net for Resource-Constrained Segmentation
  • Topology-Preserving Deep Image Segmentation
  • Show, Match and Segment: Joint Learning of Semantic Matching and Object Co-segmentation
  • DiCENet: Dimension-wise Convolutions for Efficient Networks
  • Cross-view Semantic Segmentation for Sensing Surroundings
  • NAS-FCOS: Fast Neural Architecture Search for Object Detection
  • Gated CRF Loss for Weakly Supervised Semantic Image Segmentation
  • Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions
  • Zero-Shot Semantic Segmentation
  • Consistency regularization and CutMix for semi-supervised semantic segmentation
  • RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving
  • Zero-Shot Semantic Segmentation
  • Closed-Loop Adaptation for Weakly-Supervised Semantic Segmentation
  • Closed-Loop Adaptation for Weakly-Supervised Semantic Segmentation
  • Incorporating Human Domain Knowledge in 3D LiDAR-based Semantic Segmentation
  • U-Net Based Multi-instance Video Object Segmentation(May 2019)
  • Boundary Loss for Remote Sensing Imagery Semantic Segmentation(May 2019)
  • Efficient Ladder-style DenseNets for Semantic Segmentation of Large Images(May 2019)
  • Simultaneous Object Detection and Semantic Segmentation
  • Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images
  • EdgeSegNet: A Compact Network for Semantic Segmentation(May 2019)
  • SEMEDA: Enhancing Segmentation Precision with Semantic Edge Aware Loss
  • Segmenting the Future(Apr 2019)[Code]
  • CaseNet: Content-Adaptive Scale Interaction Networks for Scene Parsing(Apr 2019)
  • ACE: Adapting to Changing Environments for Semantic Segmentation(Apr 2019)
  • FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation(Mar 2019)[Pytorch]
  • Architecture Search of Dynamic Cells for Semantic Video Segmentation(Apr 2019)
  • Template-Based Automatic Search of Compact Semantic Segmentation Architectures(Apr 2019)
  • DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation(Apr 2019)
  • DADA: Depth-aware Domain Adaptation in Semantic Segmentation(Apr 2019)
  • GFF: Gated Fully Fusion for Semantic Segmentation(Apr 2019)
  • MAVNet: an Effective Semantic Segmentation Micro-Network for MAV-based Tasks(Apr 2019)
  • Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation(Apr 2019)
  • The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation(Apr 2019)
  • Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes(Apr 2019)
  • The iterative convolution-thresholding method (ICTM) for image segmentation(Apr 2019)
  • Blurring the Line Between Structure and Learning to Optimize and Adapt Receptive Fields( Apr 2019)

2018

• CVPR 2018

  • Context Encoding for Semantic Segmentation(Mar 2018)
  • DenseASPP for Semantic Segmentation in StreetScenes[Pytorch]
  • PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing(May 2018)
  • Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation
  • DFN:Learning a Discriminative Feature Network for Semantic Segmentation
  • Guided Proofreading of Automatic Segmentations for Connectomics
  • Recurrent Scene Parsing with Perspective Understanding in the Loop
  • Context Contrasted Feature and Gated Multi-scale Aggregation for Scene Segmentation
  • icient interactive annotation of segmentation datasets with polygon rnn++
  • Compassionately Conservative Balanced Cuts for Image Segmentation
  • Dynamic-structured Semantic Propagation Network
  • In-Place Activated BatchNorm for Memory-Optimized Training of DNNs
  • Error Correction for Dense Semantic Image Labeling
  • Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation
  • On the Importance of Label Quality for Semantic Segmentation
  • Referring Image Segmentation via Recurrent Refinement Networks[Code]
  • Learning Superpixels with Segmentation-Aware Affinity Loss
  • Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer
  • Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning
  • Learning Pixel-Level Semantic Affinity With Image-Level Supervision for Weakly Supervised Semantic Segmentation
  • Weakly-Supervised Semantic Segmentation Network With Deep Seeded Region Growing
  • Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation
  • Bootstrapping the Performance of Webly Supervised Semantic Segmentation
  • Normalized Cut Loss for Weakly-Supervised CNN Segmentation
  • Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features
  • Weakly Supervised Instance Segmentation Using Class Peak Response

• ECCV 2018

  • BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation(Aug 2018)
  • ICNet for Real-Time Semantic Segmentation on High-Resolution Images(Apr 2017)[Pytorch]
  • ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation(Mar 2018 )[Pytorch]
  • Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation(Feb 2018)[Deeplab V3+]
  • Pyramid Attention Network for Semantic Segmentation
  • Adaptive Affinity Fields for Semantic Segmentation(Mar 2018)
  • ExFuse: Enhancing Feature Fusion for Semantic Segmentation(Apr 2018)
  • Unified Perceptual Parsing for Scene Understanding(Jul 2018)
  • Multi-Scale Context Intertwining for Semantic Segmentation
  • PSANet: Point-wise Spatial Attention Network for Scene Parsing

• NeurIPS 2018

  • Searching for Efficient Multi-Scale Architectures for Dense Image Prediction(Sep 2018)[Tensorflow]
  • A Probabilistic U-Net for Segmentation of Ambiguous Images(Jun 2018)
  • DifNet: Semantic Segmentation by DiffusionNetworks(May 2018)

• AAAI 2018

  • Spatial As Deep: Spatial CNN for Traffic Scene Understanding(Dec 2017)
  • Mix-and-Match Tuning for Self-Supervised Semantic Segmentation(Dec 2017)
  • Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
  • A^2-Nets: Double Attention Networks
  • Symbolic Graph Reasoning Meets Convolutions
  • Beyond Grids: Learning Graph Representations for Visual Recognition

• IJCAI 2018

  • High Resolution Feature Recovering for Accelerating Urban Scene Parsing

• Othes

  • SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous DrivingCode
  • Segmenting Objects in Day and Night:Edge-Conditioned CNN for Thermal Image Semantic Segmentation
  • [RelationNet: Learning Deep-Aligned Representation for Semantic Image Segmentation][ICPR]
  • CCNet: Criss-Cross Attention for Semantic Segmentation(Nov 2018)[Pytorch]
  • OCNet: Object Context Network for Scene Parsing(Sep 2018)[Pytorch]
  • CGNet: A Light-weight Context Guided Network for Semantic Segmentation(Nov 2018)[Pytorch]
  • ShelfNet for Real-time Semantic Segmentation, Multi-path segmentation network(Nov 2018)[Pytorch]
  • Evaluating Bayesian Deep Learning Methods for Semantic Segmentation(Nov 2018)
  • Improving Semantic Segmentation via Video Propagation and Label RelaxationCode
  • Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation(Mar 2018)
  • Locally Adaptive Learning Loss for Semantic Image Segmentation(Feb 2018)
  • RTSeg: Real-time Semantic Segmentation Comparative Study( Mar 2018)

2017

• CVPR 2017

  • Pyramid Scene Parsing Network(Dec 2016)
  • Dilated Residual Networks(Jun 2016)
  • Convolutional RandomWalk Networks for Semantic Image Segmentation(May 2016)
  • Loss Max-Pooling for Semantic Image Segmentation(Apr 2017)
  • Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes(Nov 2016)
  • Gated Feedback Refinement Network for Dense Image Labeling
  • Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
  • Semantic Segmentation via Structured Patch Prediction, Context CRF and Guidance CRF
  • Learning Adaptive Receptive Fields for Deep Image Parsing Network
  • The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation[Workshop]

• ICCV 2017

  • Segmentation-Aware Convolutional Networks Using Local Attention Mask(Mar 2017)
  • Predicting Deeper into the Future of Semantic Segmentation(Mar 2017)
  • FoveaNet: Perspective-aware Urban Scene Parsing(Aug 2017))
  • Dense and Low-Rank Gaussian CRFs Using Deep Embeddings Siddhartha
  • Scale-adaptive Convolutions for Scene Parsing
  • Deep Dual Learning for Semantic Image Segmentation
  • Semi Supervised Semantic Segmentation Using Generative Adversarial Network

• NIPS 2017

  • Learning Affinity via Spatial Propagation Networks
  • Dual Path Networks

• Others

  • Understanding Convolution for Semantic Segmentation[WACV ]
  • Semantic Segmentation with Reverse Attention(Jul 2017)[BMVC]
  • Rethinking Atrous Convolution for Semantic Image Segmentation(Jun 2017)
  • Pixel Deconvolutional Networks(May 2017)

2016

• CVPR 2016
  • Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform(Nov 2015)
  • ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation(Nov 2015)[Workshop][Pytorch]
• ECCV 2016
  • Attention to Scale: Scale-aware Semantic Image Segmentation(Nov 2015)
  • Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
  • Semantic Object Parsing with Graph LSTM(Mar 2016)
• ICLR 2016
  • Multi-scale context aggregation by dilated convolutions(Nov 2015) [Pytorch]
  • Learning Dense Convolutional Embeddings for Semantic Segmentation(Nov 2015)
• NIPS Workshop
  • Semantic Segmentation using Adversarial Networks(Nov 2016)
• others
  • ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation(Jun 2016)
  • High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks(Apr 2016)
  • PixelNet: Towards a General Pixel-level Architecture(Sep 2016))
  • MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving(Dec 2016)

2015

• CVPR 2015
  • Fully Convolutional Networks for Semantic Segmentation(Nov 2014)
  • Feedforward semantic segmentation with zoom-out features(Dec 2014)
  • Learning to Propose Objects[Project][Pytorch]
  • Hypercolumns for Object Segmentation and Fine-grained Localization
  • Scene Labeling with LSTM Recurrent Neural Networks
  • Weakly supervised semantic segmentation for social images
• ICCV 2015
  • Learning deconvolution network for semantic segmentation(May 2015)
  • Semantic Image Segmentation via Deep Parsing Network(Sep 2015)
• Other
  • Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs(Dec 2014[ICLR][DeepLabv1]
  • U-Net: Convolutional Networks for Biomedical Image Segmentation(May 2015)[MICCAI]
  • SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation(Nov 2015)
  • Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation[NIPS]

Before 2015

• paper
  • Simultaneous Detection and Segmentation(Jul 2014)[ECCV 2014]
  • Dense Segmentation-aware Descriptors[CVPR 2013]
  • Semantic Segmentation with Second-Order Pooling[ECCV2012]
  • Nonparametric Scene Parsing via Label Transfer[TPAMI2011][Project]

2D Medical Segmentation

2019

• ICCV 2019

  • Eyenet: Attention based Convolutional Encoder-Decoder Network for Eye Region Segmentation
  • Deep Plug-and-Play Prior for Parallel MRI Reconstruction
  • CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation
  • Multi-Stage Pathological Image Classification using Semantic Segmentation

• NIPS 2019

  • Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands[Code]

• MICCAI 2019

  • Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation
  • PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation[MMMI2019 Best Student Paper Award]
  • Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations
  • Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images
  • Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation
  • IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation
  • Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge[Challenge]
  • Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation
  • U-Net Fixed-Point Quantization for Medical Image SegmentationCode[Workshop]
  • Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network
  • Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation[Code]
  • Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation
  • NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentation
  • Multi-task Localization and Segmentation for X-ray Guided Planning in Knee Surgery
  • Mixed-Supervised Dual-Network for Medical Image Segmentation
  • Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation[Code]
  • INN: Inflated Neural Networks for IPMN Diagnosis(30 Jun)[Code]
  • Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders
  • Supervised Uncertainty Quantification for Segmentation with Multiple Annotations
  • Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography[Workshop]
  • U-Net Training with Instance-Layer Normalization[[Workshop]]
  • Estimation of preterm birth markers with U-Net segmentation network[[Workshop]]
  • A Weakly Supervised Method for Instance Segmentation of Biological Cells[[Workshop]][Weakly Supervised]
  • Weakly supervised segmentation from extreme points[Workshop][Weakly supervised]
  • A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes[[oral]]
  • CELNet: Evidence Localization for Pathology Images using Weakly Supervised Learning[Weakly Supervised]
  • Cardiac Segmentation of LGE MRI with Noisy Labels[Workshop]
  • Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Voting
  • Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation[Pytorch][Dataset]
  • Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation
  • Attention Guided Network for Retinal Image Segmentation[Pytorch]
  • ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image SegmentationCode
  • FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images[SenseTime]
  • Adversarial Learning with Multiscale Features and Kernel Factorization for Retinal Blood Vessel Segmentation(5 Jul)

• CVPR 2019

  • Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathology(Apr 2019)
  • Exploiting Computation Power of Blockchain for Biomedical Image Segmentation(Apr 2019)

• AAAI 2019

  • Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks(Apr 2019)

• ISBI2019

  • A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation(Oct 2018)[Keras]
  • Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks(Apr 2019)
  • Automatic Pulmonary Lobe Segmentation Using Deep Learning(Mar 2019)
  • Deep Learning with Anatomical Priors: Imitating Enhanced Autoencoders in Latent Space for Improved Pelvic Bone Segmentation in MRI(March 2019)
  • US-net for robust and efficient nuclei instance segmentation(Jan 2019)
  • Deep Convolutional Encoder-Decoders with Aggregated Multi-Resolution Skip Connections for Skin Lesion Segmentation
  • Mask-RCNN and U-net Ensembled for Nuclei Segmentation(Jan 2019)
  • Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
  • U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans
  • SUMNet: Fully Convolutional Model for Fast Segmentation of Anatomical Structures in Ultrasound Volumes
  • Learning Mutually Local-global U-nets For High-resolution Retinal Lesion Segmentation in Fundus Images
  • Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation
  • Residual Pyramid FCN for Robust Follicle Segmentation
  • Classification and Detection in Mammograms with Weak Supervision via Dual Branch Deep Neural(Net Apr 2019)[oral]

• EMBC 2019

  • Ultrasound segmentation using U-Net: learning from simulated data and testing on real data[]
  • RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network(May 2019)

• MIDL 2019

  • Distance Map Loss Penalty Term for Semantic Segmentation
  • XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation(Apr 2019))[Pytorch]
  • Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets

• MLMI

  • Privacy-preserving Federated Brain Tumour Segmentation
  • Deep Active Lesion Segmentation
  • Weakly Supervised Segmentation by A Deep Geodesic Prior
  • Boundary Aware Networks for Medical Image Segmentation
  • Automatic Rodent Brain MRI Lesion Segmentation with Fully Convolutional Networks
  • Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset
  • Biomedical Image Segmentation by Retina-like Sequential Attention Mechanism Using Only A Few Training Images

• SCI

  • UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation[Keras][IEEE Transactions on Medical Imaging]
  • CA-RefineNet:A Dual Input WSI Image Segmentation Algorithm Based on Attention
  • Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net(Apr 2019))[IEEE Transactions on Medical Imaging,TMI]
  • CE-Net: Context Encoder Network for 2D Medical Image Segmentation(Mar 2019)[Pytorch][Pytorch2][IEEE Transactions on Medical Imaging,TMI]
  • The Mutex Watershed and its Objective: Efficient, Parameter-Free Image Partitioning[IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE]
  • Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation[IEEE Transactions on Medical Imaging,TMI]
  • Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation(May 2019)[IEEE Transactions on Medical Imaging,TMI]
  • NAS-Unet: Neural Architecture Search for Medical Image Segmentation(Apr 2019)[IEEE Access]
  • 3-D Surface Segmentation Meets Conditional Random Fields
  • Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications

• other

  • Robust Automated Thalamic Nuclei Segmentation using a Multi-planar Cascaded Convolutional Neural Network
  • Divided We Stand: A Novel Residual Group Attention Mechanism for Medical Image Segmentation
  • Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning
  • HybridNetSeg: A Compact Hybrid Network for Retinal Vessel Segmentation[Pytorch]
  • Retinal Vessel Segmentation based on Fully Convolutional Networks
  • Gland Segmentation in Histopathological Images by Deep Neural Network
  • Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database
  • MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation[Keras]
  • Unified Multi-scale Feature Abstraction for Medical Image Segmentation
  • Semantic Segmentation of Skin Lesions using a Small Data Set
  • Attention-Guided Lightweight Network for Real-Time Segmentation of Robotic Surgical Instruments
  • Lung nodule segmentation via level set machine learning
  • A Symmetric Equilibrium Generative Adversarial Network with Attention Refine Block for Retinal Vessel Segmentation
  • Intelligent image synthesis to attack a segmentation CNN using adversarial learning
  • Volume Preserving Image Segmentation with Entropic Regularization Optimal Transport and Its Applications in Deep Learning
  • RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments
  • Fuzzy Semantic Segmentation of Breast Ultrasound Image with Breast Anatomy Constraints
  • Local block-wise self attention for normal organ segmentation
  • ACE-Net: Biomedical Image Segmentation with Augmented Contracting and Expansive Paths
  • On the Evaluation and Real-World Usage Scenarios of Deep Vessel Segmentation for Funduscopy
  • Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
  • Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI
  • Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation
  • Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation
  • O-MedAL: Online Active Deep Learning for Medical Image Analysis
  • IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation
  • A unified representation network for segmentation with missing modalities
  • Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning
  • Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems
  • Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation
  • Automatic segmentation of kidney and liver tumors in CT images
  • Unsupervised Microvascular Image Segmentation Using an Active Contours Mimicking Neural Network
  • Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation
  • Regularizing Proxies with Multi-Adversarial Training for Unsupervised Domain-Adaptive Semantic Segmentation
  • A Two Stage GAN for High Resolution Retinal Image Generation and Segmentation
  • Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentation
  • Lung image segmentation by generative adversarial networks
  • Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation
  • Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images
  • Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation
  • Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalization of a Musculoskeletal Model
  • ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning
  • DSNet: Automatic Dermoscopic Skin Lesion Segmentation[Code]
  • A multi-task U-net for segmentation with lazy labels(20 Jun)
  • An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial LearningJul 2019)
  • CaDSS: Cataract Dataset for Semantic Segmentation
  • Multi-Scale Attentional Network for Multi-Focal Segmentation of Active Bleed after Pelvic Fractures
  • Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography
  • Compressed Sensing MRI via a Multi-scale Dilated Residual Convolution Network
  • V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation
  • Multi-scale guided attention for medical image segmentation[Pytorch]
  • Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images
  • A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
  • Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels
  • Segmentation of blood vessels in retinal fundus images
  • A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images(May 2019)
  • A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT(May 2019)
  • Dual-branch residual network for lung nodule segmentation(May 2019)
  • A novel algorithm for segmentation of leukocytes in peripheral blood(May 2019)
  • Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset(May 2019)
  • iRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading(May 2019)
  • Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function(May 2019)
  • T-Net: Encoder-Decoder in Encoder-Decoder architecture for the main vessel segmentation in coronary angiography
  • Breast Tumor Classification and Segmentation using Convolutional Neural Networks(May 2019)
  • nnU-Net: Breaking the Spell on Successful Medical Image Segmentation(Apr 2019)[Code]
  • Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation(Mar 2019)
  • MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation(Dec 2018)
  • Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic MRI Using Mask-RCNN(Apr 2019)
  • FatSegNet : A Fully Automated Deep Learning Pipeline for Adipose Tissue Segmentation on Abdominal Dixon MRI( Apr 2019)
  • FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation(https://arxiv.org/abs/1902.03091)
  • Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks

2018

• MICCAI
  • UNet++: A Nested U-Net Architecture for Medical Image Segmentation(Jul 2018)[Keras][Pytorch]
• IPMI 2018
  • CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation
• other
  • Attention U-Net: Learning Where to Look for the Pancreas(Apr 2018)[Pytorch]
  • Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images(Aug 2018)[Pytorch]
  • MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation
  • DUNet: A deformable network for retinal vessel segmentation
  • LADDERNET: Multi-Path Networks Based on U-Net for Medical Image Segmentation[Pytorch]
  • A Probabilistic U-Net for Segmentation of Ambiguous Images (NIPS)[tensorflow]
  • 3D RoI-aware U-Net for Accurate and Efficient Colorectal Cancer Segmentation [Pytorch]

Before 2018

• 2017
  • H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes [Keras][IEEE Transactions on Medical Imaging,TIM]
• 2016
  • V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation[Pytorch][Cafee]
  • 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation[Pytorch]
• MICCAI 2015
  • U-Net: Convolutional Networks for Biomedical Image Segmentation(May 2015)[Keras][Pytorch]

3D Medical Segmentation

2019

• MICCAI 2019 Workshop
  • Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation
  • TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks[MICCAI BrainLes 2019]
  • Semi-Supervised Variational Autoencoder for Survival Prediction
  • Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube
  • Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent DataCode
  • MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation
  • SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation
  • Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation
  • 3D U2-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation[Pytorch][S. Kevin Zhou]
  • Generative adversarial network for segmentation of motion affected neonatal brain MRI
  • Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation[Code]
  • Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders
  • Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images
  • PseudoEdgeNet: Nuclei Segmentation only with Point Annotations[weakly supervised ]
  • PHiSeg: Capturing Uncertainty in Medical Image Segmentation[Code]
  • Cardiac MRI Segmentation with Strong Anatomical Guarantees(5 Jul)
  • Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation(5 Jul)[Unsupervised]
  • Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation[Stacom 2019]
  • Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation[Stacom 2019]
  • Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data
  • Multi-step Cascaded Networks for Brain Tumor Segmentation[Code][BraTS 2019]
  • Topology-preserving augmentation for CNN-based segmentation of congenital heart defects from 3D paediatric CMR[[MICCAI PIPPI]]
  • Permutohedral Attention Module for Efficient Non-Local Neural Networks(Jul 2019)[Pytorch][Dataset]
  • A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation[Pytorch][Pytorch2]
  • X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies[Keras]
  • CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic StrokeKeras[Keras]
  • 3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI(Apr 2019)[Pytorch][online-evaluation]
  • Project & Excite Modules for Segmentation of Volumetric Medical Scans[Pytorch]
  • Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
• IPMI2019
  • Brain Tumor Segmentation on MRI with Missing Modalities(Apr 2019)
  • Accurate Nuclear Segmentation with \Center Vector Encoding
  • Learning-based Optimization of the Under-sampling Pattern in MRICode
  • Random 2.5D U-net for Fully 3D Segmentation[[Workshop]]
• ISBI2019
  • Prostate Segmentation from 3D MRI Using a Two-Stage Model and Variable-Input Based Uncertainty Measure
  • Improving Catheter Segmentation & Localization in 3D Cardiac Ultrasound Using Direction-Fused FCN
• SCI
  • Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning
  • 3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles(Mar 2019)[Pytorch][NeuroImage]
• arxiv
  • Transfer Learning with Edge Attention for Prostate MRI Segmentation
  • C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
  • Adversarial normalization for multi domain image segmentation[Adversarial ]
  • EM-NET: Centerline-Aware Mitochondria Segmentation in EM Images via Hierarchical View-Ensemble Convolutional Network
  • DARTS: DenseUnet-based Automatic Rapid Tool for brain SegmentationCode
  • Trident Segmentation CNN: A Spatiotemporal Transformation CNN for Punctate White Matter Lesions Segmentation in Preterm Neonates[Keras]
  • Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net
  • Brain Tumor Segmentation and Survival Prediction
  • 3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation
  • 3D Kidneys and Kidney Tumor Semantic Segmentation using Boundary-Aware Networks
  • Automated Multiclass Cardiac Volume Segmentation and Model Generation
  • MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks
  • An Automatic Cardiac Segmentation Framework based on Multi-sequence MR Image[Keras]
  • Hybrid Cascaded Neural Network for Liver Lesion Segmentation
  • CEREBRuM: a Convolutional Encoder-decodeR for Fully Volumetric Fast sEgmentation of BRain MRI
  • High Resolution Medical Image Analysis with Spatial Partitioning
  • Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation
  • Intensity augmentation for domain transfer of whole breast segmentation in MRI
  • Gland Segmentation in Histopathology Images Using Deep Networks and Handcrafted Features
  • Transfer Learning from Partial Annotations for Whole Brain Segmentation
  • Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation
  • Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-Sequence Cardiac MR Images Segmentation
  • A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs
  • Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation
  • Discretely-constrained deep network for weakly supervised segmentation
  • Pixel-wise Segmentation of Right Ventricle of Heart
  • Boosting Liver and Lesion Segmentation from CT Scans By Mask Mining
  • Segmentation of Multimodal Myocardial Images Using Shape-Transfer GAN
  • D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation
  • Automatic acute ischemic stroke lesion segmentation using semi-supervised learning
  • Automated Brain Tumour Segmentation Using Deep Fully Convolutional Residual Networks
  • Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning
  • An attempt at beating the 3D U-Net
  • Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation
  • A Unified Point-Based Framework for 3D Segmentation
  • 2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy
  • Convolutional neural network stacking for medical image segmentation in CT scans
  • Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders
  • FD-FCN: 3D Fully Dense and Fully Convolutional Network for Semantic Segmentation of Brain Anatomy
  • AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks
  • CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation
  • A fully 3D multi-path convolutional neural network with feature fusion and feature weighting for automatic lesion identification in brain MRI images
  • Brain Tissues Segmentation on MR Perfusion Images Using CUSUM Filter for Boundary Pixels
  • Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior(29 Jun )
  • Improving the generalizability of convolutional neural network-based segmentation on CMR images
  • CSSegNet: Fine-Grained Cardiac Structures Segmentation Using Dilated Pyramid Pooling in U-net
  • Anatomically Consistent Segmentation of Organs at Risk in MRI with Convolutional Neural Networks
  • https://arxiv.org/ftp/arxiv/papers/1906/1906.10486.pdf
  • Scalable Neural Architecture Search for 3D Medical Image Segmentation
  • Enforcing temporal consistency in Deep Learning segmentation of brain MR images
  • 4D CNN for semantic segmentation of cardiac volumetric sequences
  • Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors
  • A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation
  • OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images
  • AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation
  • Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks(Jun 2019)[ cross-validation]
  • Generative Model-Based Ischemic Stroke Lesion Segmentation(Jun 2019)
  • Automated Segmentation for Hyperdense Middle Cerebral Artery Sign of Acute Ischemic Stroke on Non-Contrast CT Images(May 2019)
  • 3D Dense Separated Convolution Module for Volumetric Image Analysis(May 2019)[[cross validation]]
  • RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud（May 2019)
  • Thickened 2D Networks for 3D Medical Image Segmentation(Apr 2019)
  • Spherical U-Net on Cortical Surfaces: Methods and Applications(Apr 2019)[cross-validation]
  • Fully Automatic Segmentation of 3D Brain Ultrasound: Learning from Coarse Annotations(Apr 2019)[cross-validation]

2018

• MICCAI 2018
  • Multi-Task Learning for Left Atrial Segmentation on GE-MRI(Oct 2018)[Pytorch]

Before 2018

• MICCAI
  • 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation(Jun 2016)

Brain Tissue Segmentation

• Paper
  • DeepMRSeg: A convolutional deep neural network for anatomy and abnormality segmentation on MR images
  • Brain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization
  • 3D Patchwise U-Net with Transition Layers for MR Brain Segmentation(Rank 1)[TensorFlow]
  • MixNet: Multi-modality Mix Network for Brain Segmentation(Rank3)[tensorflow]
  • Robust 3D Convolutional Neural Network with Boundary Correction for Accurate Brain Tissue Segmentation(Rank3)[Keras]
  • Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net(Rank9)
  • On direct distribution matching for adapting segmentation networks(Apr 2019)[Code]

MS lesion segmentation

• Paper
  • Soft labeling by Distilling Anatomical knowledge for Improved MS Lesion Segmentation(Jan 2019)[ISBI2019]
  • Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NET(Jan 2019)
  • Multiple Sclerosis Lesion Inpainting Using Non-Local Partial Convolutions(Dec 2018)
  • A Self-Adaptive Network For Multiple Sclerosis Lesion Segmentation From Multi-Contrast MRI With Various Imaging Protocols(Nov 2018)
  • Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation(Nov 2018)[NeuroImage]
  • Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation((Aug 2018)[MICCAI 2018]

Instance Segmentation

2019

• ICCV 2019
  • InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting
• CVPR 2019
  • LVIS: A Dataset for Large Vocabulary Instance Segmentation
  • Hybrid Task Cascade for Instance Segmentation(Jan 2019)[Pytorch]
  • Pose2Seg: Detection Free Human Instance Segmentation(Mar 2018)[Code]
  • Budget-aware Semi-Supervised Semantic and Instance Segmentation[[Workshop]]
• other
  • A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation
  • Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth[Code]
  • DARNet: Deep Active Ray Network for Building Segmentation
  • YOLACT: Real-time Instance Segmentation(Apr 2019)
  • Concatenated Feature Pyramid Network for Instance Segmentation(Mar 2019)
  • Single Pixel Reconstruction for One-stage Instance Segmentation(Apr 2019
  • Learning Instance Occlusion for Panoptic Segmentation

Panoptic Segmentation

2019

• CVPR2019
  • Attention-guided Unified Network for Panoptic Segmentation
  • UPSNet: A Unified Panoptic Segmentation Network(Apr 2019)[Pytorch]
  • Panoptic Feature Pyramid Networks(Jan 2019[Kaiming He]
  • Interactive Full Image Segmentation by Considering All Regions Jointly
  • Panoptic Segmentation
• other
  • Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
  • Panoptic-DeepLab
  • [Fast Panoptic Segmentation Network](https://arxiv.org/abs/1910.03892
  • Generator evaluator-selector net: a modular approach for panoptic segmentation
  • Straight to Shapes++: Real-time Instance Segmentation Made More Accurate(May 2019)
  • DeeperLab: Single-Shot Image Parser(Feb 2019)[[Code]]
  • An End-to-End Network for Panoptic Segmentation(Mar 2019
  • Single Network Panoptic Segmentation for Street Scene Understanding
  • Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network(Feb 2019)
  • Detecting Reflections by Combining Semantic and Instance Segmentation(Apr 2019)
  • PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things(March 2019)
  • Class-independent sequential full image segmentation, using a convolutional net that finds a segment within an attention region, given a pointer pixel within this segment(February 2019

2018

• ECCV2018
  • Weakly- and Semi-Supervised Panoptic Segmentation(Aug 2018) other
  • Learning to Fuse Things and Stuff(Dec 2018)

Video-Segmentation

2019

• NIPS
  • MetaPix: Few-Shot Video Retargeting[Code][Workshop]
  • LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition
• ICCV 2019
  • Anchor Diffusion for Unsupervised Video Object Segmentation
  • CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing[Code]
  • DMM-Net: Differentiable Mask-Matching Network for Video Object SegmentationPytorch
  • RANet: Ranking Attention Network for Fast Video Object Segmentation[Cpde]
  • Exploiting Temporality for Semi-Supervised Video Segmentation
• CVPR 2019
  • Spatiotemporal CNN for Video Object Segmentation(Apr 2019)[Code]
  • MHP-VOS: Multiple Hypotheses Propagation for Video Object Segmentation[Code]
• other
  • RPM-Net: Robust Pixel-Level Matching Networks for Self-Supervised Video Object Segmentation
  • Towards Good Practices for Video Object Segmentation
  • Towards Good Practices for Video Object Segmentation
  • Fast Video Object Segmentation via Mask Transfer Network
  • Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation(Jul 2019)
  • Video Instance Segmentation
  • MAIN: Multi-Attention Instance Network for Video Segmentation

Re-Identification

2019

• ICCV
  • ABD-Net: Attentive but Diverse Person Re-IdentificationCode
  • Discriminative Feature Learning With Consistent Attention Regularization for Person Re-Identification
  • self-Critical Attention Learning for Person Re-Identification
  • Attention Bridging Network for Knowledge Transfer
  • Attentional Feature-Pair Relation Networks for Accurate Face Recognition
  • Towards Interpretable Face Recognition
  • Mixed High-Order Attention Network for Person Re-Identification[Code]
  • Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identificationCode[Oral]
• Awesome
  + Awesome Person Re-identification (Person ReID)

2018

• A2-Nets: Double Attention Networks[NIPS]
• CBAM: Convolutional Block Attention Module[ECCV]

2017

• SVDNet for Pedestrian Retrieval