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<!DOCTYPE html>
<html>
<head lang="en">
<meta charset="UTF-8">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<title>NETS</title>
<meta name="description" content="">
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</head>
<body font-family: 'Didact Gothic'>
<div class="container">
<div class="row">
<h2 class="col-md-12 text-center" font-family: 'Didact Gothic'>
From Forks to Forceps: A New Framework for Instance Segmentation of Surgical Instruments
</br>
<small>
WACV 2023
</small>
</h2>
</div>
<div class="row">
<div class="col-md-12 text-center">
<ul class="list-inline">
<li>
<a href="https://www.cse.iitd.ac.in/~britty">
Britty Baby
</a>
</br>IIT Delhi
</li>
<li>
<a href="https://www.cse.iitm.ac.in/~daksh">
Daksh Thapar
</a>
</br>IIT Mandi
</li>
<li>
<a href="mailto:[email protected]">
Mustafa Chasmai
</a>
</br>IIT Delhi
<li>
<a href="mailto:[email protected]">
Tamajit Banerjee
</a>
</br>IIT Delhi
</li>
<li>
<a href="https://www.cse.iitd.ac.in/~kunal">
Kunal Dargan
</a>
</br>IIT Delhi
</li>
<li>
<a href="mailto:[email protected]">
Ashish Suri
</a>
</br>AIIMS New Delhi
</li>
<li>
<a href="https://www.cse.iitd.ac.in/~suban">
Subhashis Banerjee
</a>
</br>IIT Delhi
</li>
<li>
<a href="https://www.cse.iitd.ac.in/~chetan">
Chetan Arora
</a>
</br>IIT Delhi
</li>
</ul>
</div>
</div>
<div class="row" align="middle">
<div class="btn-group" role="group" aria-label="Top menu" align="middle">
<a class="btn btn-primary" href="https://arxiv.org/pdf/2211.16200.pdf">Paper</a>
<a class="btn btn-primary" href="https://github.com/myselfbritty/S3Net">Code</a>
<a class="btn btn-primary" href="https://drive.google.com/drive/folders/1pU1eWmYPJOwiaP7XKPjhZriZ1Anc-9dm?usp=sharing">Dataset</a>
</div>
</div>
<br><br>
<div class="row" id="header_img" align="middle">
<image src="S3Net/teaser.jpg" class="img-responsive" alt="overview" width="700px">
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h4>
Abstract
</h4>
<p class="text-justify" font-family: 'Didact Gothic'>
Minimally invasive surgeries and related applications demand surgical tool classification and segmentation at the
instance level. Surgical tools are similar in appearance and are often long, thin, and handled at an angle. The
fine-tuning of state-of-the-art (SOTA) instance segmentation models trained on natural images for instrument segmentation has difficulty differentiating between instrument
classes. Our research demonstrates that while the bounding box and segmentation mask are often accurate, the classification
head misclassifies the class label of the surgical instrument. Based on this insight, we present a new neural
network framework that adds a classification module as a new stage to existing instance segmentation models. This
module specializes in improving the classification of instrument masks generated by the existing model. The module
comprises multi-scale mask attention, which attends to the instrument region and masks the distracting background
features. We propose training the proposed classifier module using metric learning with arc loss to handle low inter-
class variance of surgical instruments. We conduct exhaustive experiments on the benchmark datasets EndoVis2017
and EndoVis2018. We demonstrate that our method outperforms all (more than 20) SOTA methods compared with
and improves the SOTA performance by at least 12 points (20 percent) on the EndoVis2017 benchmark challenge and
generalizes effectively across the datasets.
</p>
</div>
</div>
<!--
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<div class="col-md-8 col-md-offset-2">
<h4>
Key Results
</h4>
<div class="row" id="result_img" align="middle">
<image src="nets-data/key-result.png" class="img-responsive" alt="results" width="600px">
</div>
</div>
</div> -->
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h4>
BibTeX (Citation)
</h4>
<pre style="white-space: pre-wrap; background: hsl(220, 50%, 95%); font-size: 11px">
@inproceedings{baby2023forks,
title={From Forks to Forceps: A New Framework for Instance Segmentation of Surgical Instruments},
author={Baby, Britty and Thapar, Daksh and Chasmai, Mustafa and Banerjee, Tamajit and Dargan, Kunal and Suri, Ashish and Banerjee, Subhashis and Arora, Chetan},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={6191--6201},
year={2023}
}
</pre>
</div>
</div>
<!--div class="row">
<div class="col-md-8 col-md-offset-2">
<h4 font-family: 'Didact Gothic'>
Acknowledgements
</h5>
<p class="text-justify" font-family: 'Didact Gothic'>
This work was supported by ???.
</p>
</div>
</div-->
<div class="row">
<div class="col-md-8 col-md-offset-2">
<p class="text-justify" font-family: 'Didact Gothic'>
<center style="font-size:10px"><b>Credits:</b> Template of this webpage from <a href="http://www.mgharbi.com/">
here.
</a></center>
</p>
</div>
</div>
</div>
</body>
</html>