diff --git a/dataset.py b/dataset.py
index ae794301..ddd4ceff 100644
--- a/dataset.py
+++ b/dataset.py
@@ -47,7 +47,6 @@ def __getitem__(self, index):
         # else:
         #     inout = 1
         #     point_label = 1
-        inout = 1
         point_label = 1
 
         """Get the images"""
@@ -69,7 +68,7 @@ def __getitem__(self, index):
         mask = mask.resize(newsize)
 
         if self.prompt == 'click':
-            pt = random_click(np.array(mask) / 255, point_label, inout)
+            point_label, pt = random_click(np.array(mask) / 255, point_label)
 
         if self.transform:
             state = torch.get_rng_state()
@@ -110,7 +109,6 @@ def __len__(self):
         return len(self.subfolders)
 
     def __getitem__(self, index):
-        inout = 1
         point_label = 1
 
         """Get the images"""
@@ -132,10 +130,10 @@ def __getitem__(self, index):
         multi_rater_cup_np = [np.array(single_rater.resize(newsize)) for single_rater in multi_rater_cup]
         multi_rater_disc_np = [np.array(single_rater.resize(newsize)) for single_rater in multi_rater_disc]
 
-        # first click is the target agreement among all raters
+        # first click is the target agreement among most raters
         if self.prompt == 'click':
-            pt_cup = random_click(np.array(np.mean(np.stack(multi_rater_cup_np), axis=0)) / 255, point_label, inout)
-            pt_disc = random_click(np.array(np.mean(np.stack(multi_rater_disc_np), axis=0)) / 255, point_label, inout)
+            point_label, pt_cup = random_click(np.array(np.mean(np.stack(multi_rater_cup_np), axis=0)) / 255, point_label)
+            point_label, pt_disc = random_click(np.array(np.mean(np.stack(multi_rater_disc_np), axis=0)) / 255, point_label)
 
         if self.transform:
             state = torch.get_rng_state()
@@ -153,16 +151,93 @@ def __getitem__(self, index):
         image_meta_dict = {'filename_or_obj':name}
         return {
             'image':img,
-            'multi_rater_cup': multi_rater_cup,
+            'multi_rater': multi_rater_cup,
             'multi_rater_disc': multi_rater_disc,
             'mask_cup': mask_cup,
             'mask_disc': mask_disc,
-            'label': mask_disc,
+            'label': mask_cup,
             'p_label':point_label,
             'pt_cup':pt_cup,
             'pt_disc':pt_disc,
-            'pt':pt_disc,
-            'selected_rater': torch.tensor(np.arange(7)),
+            'pt':pt_cup,
+            'image_meta_dict':image_meta_dict,
+        }
+    
+
+class LIDC(Dataset):
+    names = []
+    images = []
+    labels = []
+    series_uid = []
+
+    def __init__(self, data_path, transform=None, transform_msk = None, prompt = 'click'):
+        self.prompt = prompt
+        self.transform = transform
+        self.transform_msk = transform_msk
+        
+        max_bytes = 2**31 - 1
+        data = {}
+        for file in os.listdir(data_path):
+            filename = os.fsdecode(file)
+            if '.pickle' in filename:
+                file_path = data_path + filename
+                bytes_in = bytearray(0)
+                input_size = os.path.getsize(file_path)
+                with open(file_path, 'rb') as f_in:
+                    for _ in range(0, input_size, max_bytes):
+                        bytes_in += f_in.read(max_bytes)
+                new_data = pickle.loads(bytes_in)
+                data.update(new_data)
+                
+        
+        for key, value in data.items():
+            self.names.append(key)
+            self.images.append(value['image'].astype(float))
+            self.labels.append(value['masks'])
+            self.series_uid.append(value['series_uid'])
+
+        assert (len(self.images) == len(self.labels) == len(self.series_uid))
+
+        for img in self.images:
+            assert np.max(img) <= 1 and np.min(img) >= 0
+        for label in self.labels:
+            assert np.max(label) <= 1 and np.min(label) >= 0
+
+        del new_data
+        del data
+
+    def __len__(self):
+        return len(self.images)
+
+    def __getitem__(self, index):
+
+        point_label = 1
+
+        """Get the images"""
+        img = np.expand_dims(self.images[index], axis=0)
+        name = self.names[index]
+        multi_rater = self.labels[index]
+
+        # first click is the target most agreement among raters, otherwise, background agreement
+        if self.prompt == 'click':
+            point_label, pt = random_click(np.array(np.mean(np.stack(multi_rater), axis=0)) / 255, point_label)
+
+        # Convert image (ensure three channels) and multi-rater labels to torch tensors
+        img = torch.from_numpy(img).type(torch.float32)
+        img = img.repeat(3, 1, 1) 
+        multi_rater = [torch.from_numpy(single_rater).type(torch.float32) for single_rater in multi_rater]
+
+        multi_rater = torch.stack(multi_rater, dim=0)
+        multi_rater = multi_rater.unsqueeze(1)
+        mask = multi_rater.mean(dim=0) # average
+
+        image_meta_dict = {'filename_or_obj':name}
+        return {
+            'image':img,
+            'multi_rater': multi_rater,
+            'label': mask,
+            'p_label':point_label,
+            'pt':pt,
             'image_meta_dict':image_meta_dict,
         }
         
diff --git a/environment.yml b/environment.yml
index f2747d7c..4f352008 100644
--- a/environment.yml
+++ b/environment.yml
@@ -20,11 +20,12 @@ dependencies:
   - brunsli=0.1=h2531618_0
   - bzip2=1.0.8=h7b6447c_0
   - c-ares=1.19.0=h5eee18b_0
-  - ca-certificates=2022.12.7=ha878542_0
+  - ca-certificates=2023.11.17=hbcca054_0
   - cffi=1.15.1=py310h5eee18b_3
   - cfitsio=3.470=h5893167_7
   - charls=2.2.0=h2531618_0
   - cloudpickle=2.2.1=py310h06a4308_0
+  - comm=0.1.4=pyhd8ed1ab_0
   - contourpy=1.0.5=py310hdb19cb5_0
   - cpuonly=2.0=0
   - cryptography=39.0.1=py310h9ce1e76_0
@@ -32,6 +33,8 @@ dependencies:
   - cytoolz=0.12.0=py310h5eee18b_0
   - dask-core=2023.4.1=py310h06a4308_0
   - dbus=1.13.18=hb2f20db_0
+  - debugpy=1.6.7=py310h6a678d5_0
+  - decorator=5.1.1=pyhd8ed1ab_0
   - expat=2.4.9=h6a678d5_0
   - ffmpeg=4.3=hf484d3e_0
   - fontconfig=2.14.1=h4c34cd2_2
@@ -51,9 +54,13 @@ dependencies:
   - imagecodecs=2021.8.26=py310h46e8fbd_2
   - imageio=2.26.0=py310h06a4308_0
   - importlib-metadata=6.0.0=py310h06a4308_0
+  - importlib_metadata=6.0.0=hd8ed1ab_0
   - intel-openmp=2021.4.0=h06a4308_3561
+  - ipykernel=6.26.0=pyhf8b6a83_0
   - joblib=1.1.1=py310h06a4308_0
   - jpeg=9e=h5eee18b_1
+  - jupyter_client=8.6.0=pyhd8ed1ab_0
+  - jupyter_core=5.5.0=py310hff52083_0
   - jxrlib=1.1=h7b6447c_2
   - kiwisolver=1.4.4=py310h6a678d5_0
   - krb5=1.19.4=h568e23c_0
@@ -74,6 +81,7 @@ dependencies:
   - libev=4.33=h7f8727e_1
   - libevent=2.1.12=h8f2d780_0
   - libffi=3.4.2=h6a678d5_6
+  - libgcc=7.2.0=h69d50b8_2
   - libgcc-ng=11.2.0=h1234567_1
   - libgfortran-ng=11.2.0=h00389a5_1
   - libgfortran5=11.2.0=h1234567_1
@@ -85,6 +93,7 @@ dependencies:
   - libpng=1.6.39=h5eee18b_0
   - libpq=12.9=h16c4e8d_3
   - libprotobuf=3.20.3=he621ea3_0
+  - libsodium=1.0.18=h36c2ea0_1
   - libssh2=1.10.0=h8f2d780_0
   - libstdcxx-ng=11.2.0=h1234567_1
   - libtasn1=4.19.0=h5eee18b_0
@@ -104,13 +113,16 @@ dependencies:
   - markupsafe=2.1.1=py310h7f8727e_0
   - matplotlib=3.7.1=py310h06a4308_1
   - matplotlib-base=3.7.1=py310h1128e8f_1
+  - matplotlib-inline=0.1.6=pyhd8ed1ab_0
   - mkl=2021.4.0=h06a4308_640
   - mkl-service=2.4.0=py310h7f8727e_0
   - mkl_fft=1.3.1=py310hd6ae3a3_0
   - mkl_random=1.2.2=py310h00e6091_0
   - monai=1.1.0=pyhd8ed1ab_0
   - multidict=6.0.2=py310h5eee18b_0
+  - munkres=1.1.4=pyh9f0ad1d_0
   - ncurses=6.4=h6a678d5_0
+  - nest-asyncio=1.5.8=pyhd8ed1ab_0
   - nettle=3.7.3=hbbd107a_1
   - networkx=2.8.4=py310h06a4308_1
   - nspr=4.33=h295c915_0
@@ -121,24 +133,35 @@ dependencies:
   - oauthlib=3.2.2=py310h06a4308_0
   - openh264=2.1.1=h4ff587b_0
   - openjpeg=2.4.0=h3ad879b_0
-  - openssl=1.1.1t=h7f8727e_0
+  - openssl=1.1.1w=h7f8727e_0
   - packaging=23.0=py310h06a4308_0
   - pandas=1.5.3=py310h1128e8f_0
+  - parso=0.8.3=pyhd8ed1ab_0
   - pcre=8.45=h295c915_0
+  - pexpect=4.8.0=pyh1a96a4e_2
+  - pickleshare=0.7.5=py_1003
   - pillow=9.4.0=py310h6a678d5_0
   - pip=23.0.1=py310h06a4308_0
+  - platformdirs=4.1.0=pyhd8ed1ab_0
   - ply=3.11=py310h06a4308_0
   - protobuf=3.20.3=py310h6a678d5_0
+  - ptyprocess=0.7.0=pyhd3deb0d_0
+  - pure_eval=0.2.2=pyhd8ed1ab_0
+  - pycparser=2.21=pyhd8ed1ab_0
   - pyjwt=2.4.0=py310h06a4308_0
   - pyopenssl=23.0.0=py310h06a4308_0
   - pyparsing=3.0.9=py310h06a4308_0
   - pyqt=5.15.7=py310h6a678d5_1
   - pysocks=1.7.1=py310h06a4308_0
   - python=3.10.11=h7a1cb2a_2
+  - python-dateutil=2.8.2=pyhd8ed1ab_0
+  - python_abi=3.10=2_cp310
   - pytorch-mutex=1.0=cpu
   - pytz=2022.7=py310h06a4308_0
+  - pyu2f=0.1.5=pyhd8ed1ab_0
   - pywavelets=1.4.1=py310h5eee18b_0
   - pyyaml=6.0=py310h5eee18b_1
+  - pyzmq=23.0.0=py310h330234f_0
   - qt-main=5.15.2=h8373d8f_8
   - qt-webengine=5.15.9=hbbf29b9_6
   - qtwebkit=5.212=h3fafdc1_5
@@ -151,153 +174,146 @@ dependencies:
   - seaborn=0.12.2=py310h06a4308_0
   - setuptools=66.0.0=py310h06a4308_0
   - sip=6.6.2=py310h6a678d5_0
+  - six=1.16.0=pyh6c4a22f_0
   - snappy=1.1.9=h295c915_0
   - sqlite=3.41.2=h5eee18b_0
+  - stack_data=0.6.2=pyhd8ed1ab_0
   - tensorboard=2.11.0=py310h06a4308_0
   - tensorboard-data-server=0.6.1=py310h52d8a92_0
   - tensorboard-plugin-wit=1.8.1=py310h06a4308_0
   - tk=8.6.12=h1ccaba5_0
+  - toml=0.10.2=pyhd8ed1ab_0
   - toolz=0.12.0=py310h06a4308_0
   - torchaudio=0.12.1=py310_cpu
   - tornado=6.2=py310h5eee18b_0
   - tqdm=4.65.0=py310h2f386ee_0
   - typing_extensions=4.5.0=py310h06a4308_0
   - tzdata=2023c=h04d1e81_0
+  - unicodedata2=14.0.0=py310h5764c6d_1
   - urllib3=1.26.15=py310h06a4308_0
   - wheel=0.38.4=py310h06a4308_0
   - xz=5.4.2=h5eee18b_0
   - yaml=0.2.5=h7b6447c_0
   - yarl=1.8.1=py310h5eee18b_0
+  - zeromq=4.3.4=h9c3ff4c_1
   - zfp=0.5.5=h295c915_6
   - zipp=3.11.0=py310h06a4308_0
   - zlib=1.2.13=h5eee18b_0
   - zstd=1.5.5=hc292b87_0
   - pip:
-    - --extra-index-url https://download.pytorch.org/whl/cu113
-    - aiosignal==1.2.0
-    - alembic==1.10.4
-    - appdirs==1.4.4
-    - astor==0.8.1
-    - asttokens==2.2.1
-    - backcall==0.2.0
-    - beautifulsoup4==4.12.2
-    - blinker==1.6.2
-    - cachetools==4.2.2
-    - certifi==2022.12.7
-    - charset-normalizer==2.0.4
-    - click==8.1.3
-    - cmaes==0.9.1
-    - colorama==0.4.6
-    - colorlog==6.7.0
-    - contextlib2==21.6.0
-    - coverage==6.5.0
-    - coveralls==3.3.1
-    - cucim==23.4.1
-    - cycler==0.11.0
-    - databricks-cli==0.17.7
-    - decorator==5.1.1
-    - docker==6.1.1
-    - docopt==0.6.2
-    - einops==0.6.1
-    - entrypoints==0.4
-    - exceptiongroup==1.1.1
-    - executing==1.2.0
-    - filelock==3.12.0
-    - fire==0.5.0
-    - flask==2.3.2
-    - fonttools==4.25.0
-    - future==0.18.3
-    - gdown==4.7.1
-    - gitdb==4.0.10
-    - gitpython==3.1.31
-    - google-auth==2.6.0
-    - google-auth-oauthlib==0.4.4
-    - greenlet==2.0.2
-    - gunicorn==20.1.0
-    - h5py==3.8.0
-    - huggingface-hub==0.14.1
-    - iniconfig==2.0.0
-    - ipython==8.13.1
-    - itk==5.3.0
-    - itk-core==5.3.0
-    - itk-filtering==5.3.0
-    - itk-io==5.3.0
-    - itk-numerics==5.3.0
-    - itk-registration==5.3.0
-    - itk-segmentation==5.3.0
-    - itsdangerous==2.1.2
-    - jedi==0.18.2
-    - jinja2==3.1.2
-    - json-tricks==3.16.1
-    - jsonschema==4.17.3
-    - kornia==0.4.1
-    - lmdb==1.4.1
-    - lucent==0.1.0
-    - mako==1.2.4
-    - matplotlib-inline==0.1.6
-    - mlflow==2.3.1
-    - munkres==1.1.4
-    - nibabel==5.1.0
-    - ninja==1.11.1
-    - nni==2.10
-    - nptyping==2.5.0
-    - opencv-python==4.7.0.72
-    - openslide-python==1.1.2
-    - optuna==3.1.1
-    - parso==0.8.3
-    - partd==1.2.0
-    - pexpect==4.8.0
-    - pickleshare==0.7.5
-    - pluggy==1.0.0
-    - pooch==1.4.0
-    - prettytable==3.7.0
-    - prompt-toolkit==3.0.38
-    - psutil==5.9.5
-    - ptyprocess==0.7.0
-    - pure-eval==0.2.2
-    - pyarrow==11.0.0
-    - pyasn1==0.4.8
-    - pyasn1-modules==0.2.8
-    - pycparser==2.21
-    - pydicom==2.3.1
-    - pygments==2.15.1
-    - pynrrd==1.0.0
-    - pyqt5-sip==12.11.0
-    - pyrsistent==0.19.3
-    - pytest==7.3.1
-    - pytest-mock==3.10.0
-    - python-dateutil==2.8.2
-    - pythonwebhdfs==0.2.3
-    - pytorch-ignite==0.4.10
-    - querystring-parser==1.2.4
-    - regex==2023.5.5
-    - requests-oauthlib==1.3.0
-    - responses==0.23.1
-    - rsa==4.7.2
-    - schema==0.7.5
-    - simplejson==3.19.1
-    - six==1.16.0
-    - smmap==5.0.0
-    - soupsieve==2.4.1
-    - sqlalchemy==2.0.12
-    - sqlparse==0.4.4
-    - stack-data==0.6.2
-    - tabulate==0.9.0
-    - tensorboardx==2.2
-    - termcolor==2.3.0
-    - threadpoolctl==2.2.0
-    - tifffile==2021.7.2
-    - tokenizers==0.12.1
-    - toml==0.10.2
-    - tomli==2.0.1
-    - torch==1.12.1+cu113
-    - torch-lucent==0.1.8
-    - torchvision==0.13.1+cu113
-    - traitlets==5.9.0
-    - transformers==4.21.3
-    - typeguard==3.0.2
-    - types-pyyaml==6.0.12.9
-    - wcwidth==0.2.6
-    - websocket-client==1.5.1
-    - websockets==11.0.3
-    - werkzeug==2.3.4
+      - aiosignal==1.2.0
+      - alembic==1.10.4
+      - appdirs==1.4.4
+      - astor==0.8.1
+      - asttokens==2.2.1
+      - backcall==0.2.0
+      - beautifulsoup4==4.12.2
+      - blinker==1.6.2
+      - cachetools==4.2.2
+      - certifi==2022.12.7
+      - charset-normalizer==2.0.4
+      - click==8.1.3
+      - cmaes==0.9.1
+      - colorama==0.4.6
+      - colorlog==6.7.0
+      - contextlib2==21.6.0
+      - coverage==6.5.0
+      - coveralls==3.3.1
+      - cucim==23.4.1
+      - cycler==0.11.0
+      - databricks-cli==0.17.7
+      - docker==6.1.1
+      - docopt==0.6.2
+      - einops==0.6.1
+      - entrypoints==0.4
+      - exceptiongroup==1.1.1
+      - executing==1.2.0
+      - filelock==3.12.0
+      - fire==0.5.0
+      - flask==2.3.2
+      - fonttools==4.25.0
+      - future==0.18.3
+      - gdown==4.7.1
+      - gitdb==4.0.10
+      - gitpython==3.1.31
+      - google-auth==2.6.0
+      - google-auth-oauthlib==0.4.4
+      - greenlet==2.0.2
+      - gunicorn==20.1.0
+      - h5py==3.8.0
+      - huggingface-hub==0.14.1
+      - iniconfig==2.0.0
+      - ipython==8.13.1
+      - itk==5.3.0
+      - itk-core==5.3.0
+      - itk-filtering==5.3.0
+      - itk-io==5.3.0
+      - itk-numerics==5.3.0
+      - itk-registration==5.3.0
+      - itk-segmentation==5.3.0
+      - itsdangerous==2.1.2
+      - jedi==0.18.2
+      - jinja2==3.1.2
+      - json-tricks==3.16.1
+      - jsonschema==4.17.3
+      - kornia==0.4.1
+      - lmdb==1.4.1
+      - lucent==0.1.0
+      - mako==1.2.4
+      - mlflow==2.3.1
+      - nibabel==5.1.0
+      - ninja==1.11.1
+      - nni==2.10
+      - nptyping==2.5.0
+      - opencv-python==4.7.0.72
+      - openslide-python==1.1.2
+      - optuna==3.1.1
+      - partd==1.2.0
+      - pluggy==1.0.0
+      - pooch==1.4.0
+      - prettytable==3.7.0
+      - prompt-toolkit==3.0.38
+      - psutil==5.9.5
+      - pyarrow==11.0.0
+      - pyasn1==0.4.8
+      - pyasn1-modules==0.2.8
+      - pydicom==2.3.1
+      - pygments==2.15.1
+      - pynrrd==1.0.0
+      - pyqt5-sip==12.11.0
+      - pyrsistent==0.19.3
+      - pytest==7.3.1
+      - pytest-mock==3.10.0
+      - pythonwebhdfs==0.2.3
+      - pytorch-ignite==0.4.10
+      - querystring-parser==1.2.4
+      - regex==2023.5.5
+      - requests-oauthlib==1.3.0
+      - responses==0.23.1
+      - rsa==4.7.2
+      - safetensors==0.4.1
+      - schema==0.7.5
+      - simplejson==3.19.1
+      - smmap==5.0.0
+      - soupsieve==2.4.1
+      - sqlalchemy==2.0.12
+      - sqlparse==0.4.4
+      - tabulate==0.9.0
+      - tensorboardx==2.2
+      - termcolor==2.3.0
+      - threadpoolctl==2.2.0
+      - tifffile==2021.7.2
+      - timm==0.9.12
+      - tokenizers==0.12.1
+      - tomli==2.0.1
+      - torch==1.12.1+cu113
+      - torch-lucent==0.1.8
+      - torchvision==0.13.1+cu113
+      - traitlets==5.9.0
+      - transformers==4.21.3
+      - typeguard==3.0.2
+      - types-pyyaml==6.0.12.9
+      - wcwidth==0.2.6
+      - websocket-client==1.5.1
+      - websockets==11.0.3
+      - werkzeug==2.3.4
diff --git a/train.py b/train.py
index 7cbb591f..289fd005 100644
--- a/train.py
+++ b/train.py
@@ -25,6 +25,7 @@
 #from dataset import *
 from torch.autograd import Variable
 from torch.utils.data import DataLoader, random_split
+from torch.utils.data.sampler import SubsetRandomSampler
 from tqdm import tqdm
 
 import cfg
@@ -108,11 +109,26 @@
     '''REFUGE data'''
     refuge_train_dataset = REFUGE(args, args.data_path, transform = transform_train, transform_msk= transform_train_seg, mode = 'Training')
     refuge_test_dataset = REFUGE(args, args.data_path, transform = transform_test, transform_msk= transform_test_seg, mode = 'Test')
-    
+
     nice_train_loader = DataLoader(refuge_train_dataset, batch_size=args.b, shuffle=True, num_workers=8, pin_memory=True)
     nice_test_loader = DataLoader(refuge_test_dataset, batch_size=args.b, shuffle=False, num_workers=8, pin_memory=True)
     '''end'''
 
+elif args.dataset == 'LIDC':
+    '''LIDC data'''
+    dataset = LIDC(data_path = args.data_path)
+
+    dataset_size = len(dataset)
+    indices = list(range(dataset_size))
+    split = int(np.floor(0.2 * dataset_size))
+    np.random.shuffle(indices)
+    train_sampler = SubsetRandomSampler(indices[split:])
+    test_sampler = SubsetRandomSampler(indices[:split])
+
+    nice_train_loader = DataLoader(dataset, batch_size=args.b, sampler=train_sampler, num_workers=8, pin_memory=True)
+    nice_test_loader = DataLoader(dataset, batch_size=args.b, sampler=test_sampler, num_workers=8, pin_memory=True)
+    '''end'''
+
 
 '''checkpoint path and tensorboard'''
 # iter_per_epoch = len(Glaucoma_training_loader)
diff --git a/utils.py b/utils.py
index 406c9820..edb35f2e 100644
--- a/utils.py
+++ b/utils.py
@@ -1114,9 +1114,14 @@ def calculate_gradient_penalty(netD, real_images, fake_images):
     return grad_penalty
 
 
-def random_click(mask, point_labels = 1, inout = 1):
-    indices = np.argwhere(mask == inout)
-    return indices[np.random.randint(len(indices))]
+def random_click(mask, point_labels = 1):
+    # check if all masks are black
+    max_label = max(set(mask.flatten()))
+    if max_label == 0:
+        point_labels = max_label
+    # max agreement position
+    indices = np.argwhere(mask == max_label) 
+    return point_labels, indices[np.random.randint(len(indices))]
 
 
 def generate_click_prompt(img, msk, pt_label = 1):