[Project] Medical semantic seg dataset: ISIC-2016 Task1 (#2708)

projects/medical/2d_image/dermoscopy/isic2016_task1/README.md

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+# ISIC-2016 Task1
+
+## Description
+
+This project support **`ISIC-2016 Task1 `**, and the dataset used in this project can be downloaded from [here](https://challenge.isic-archive.com/data/#2016).
+
+### Dataset Overview
+
+The overarching goal of the challenge is to develop image analysis tools to enable the automated diagnosis of melanoma from dermoscopic images.
+
+This challenge provides training data (~900 images) for participants to engage in all 3 components of lesion image analysis. A separate test dataset (~350 images) will be provided for participants to generate and submit automated results.
+
+### Original Statistic Information
+
+| Dataset name                                                     | Anatomical region | Task type    | Modality   | Num. Classes | Train/Val/Test Images | Train/Val/Test Labeled | Release Date | License                                                                |
+| ---------------------------------------------------------------- | ----------------- | ------------ | ---------- | ------------ | --------------------- | ---------------------- | ------------ | ---------------------------------------------------------------------- |
+| [ISIC-2016 Task1](https://challenge.isic-archive.com/data/#2016) | full body         | segmentation | dermoscopy | 2            | 900/-/379-            | yes/-/yes              | 2016         | [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) |
+
+| Class Name  | Num. Train | Pct. Train | Num. Val | Pct. Val | Num. Test | Pct. Test |
+| :---------: | :--------: | :--------: | :------: | :------: | :-------: | :-------: |
+| background  |    900     |   82.08    |    -     |    -     |    379    |   81.98   |
+| skin lesion |    900     |   17.92    |    -     |    -     |    379    |   18.02   |
+
+Note:
+
+- `Pct` means percentage of pixels in this category in all pixels.
+
+### Visualization
+
+![bac](https://raw.githubusercontent.com/uni-medical/medical-datasets-visualization/main/2d/semantic_seg/dermoscopy/isic2016_task1/isic2016_task1.png)
+
+### Prerequisites
+
+- Python 3.8
+- PyTorch 1.10.0
+- pillow(PIL) 9.3.0
+- scikit-learn(sklearn) 1.2.0
+- [MIM](https://github.com/open-mmlab/mim) v0.3.4
+- [MMCV](https://github.com/open-mmlab/mmcv) v2.0.0rc4
+- [MMEngine](https://github.com/open-mmlab/mmengine) v0.2.0 or higher
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation) v1.0.0rc5
+
+All the commands below rely on the correct configuration of PYTHONPATH, which should point to the project's directory so that Python can locate the module files. In isic2016_task1/ root directory, run the following line to add the current directory to PYTHONPATH:
+
+```shell
+export PYTHONPATH=`pwd`:$PYTHONPATH
+```
+
+### Dataset preparing
+
+- download dataset from [here](https://challenge.isic-archive.com/data/#2016) and decompression data to path 'data/'.
+- run script `"python tools/prepare_dataset.py"` to split dataset and change folder structure as below.
+- run script `"python ../../tools/split_seg_dataset.py"` to split dataset and generate `train.txt` and `test.txt`. If the label of official validation set and test set can't be obtained, we generate `train.txt` and `val.txt` from the training set randomly.
+
+```none
+  mmsegmentation
+  ├── mmseg
+  ├── projects
+  │   ├── medical
+  │   │   ├── 2d_image
+  │   │   │   ├── dermoscopy
+  │   │   │   │   ├── isic2016_task1
+  │   │   │   │   │   ├── configs
+  │   │   │   │   │   ├── datasets
+  │   │   │   │   │   ├── tools
+  │   │   │   │   │   ├── data
+  │   │   │   │   │   │   ├── train.txt
+  │   │   │   │   │   │   ├── test.txt
+  │   │   │   │   │   │   ├── images
+  │   │   │   │   │   │   │   ├── train
+  │   │   │   │   |   │   │   │   ├── xxx.png
+  │   │   │   │   |   │   │   │   ├── ...
+  │   │   │   │   |   │   │   │   └── xxx.png
+  │   │   │   │   │   │   │   ├── test
+  │   │   │   │   |   │   │   │   ├── yyy.png
+  │   │   │   │   |   │   │   │   ├── ...
+  │   │   │   │   |   │   │   │   └── yyy.png
+  │   │   │   │   │   │   ├── masks
+  │   │   │   │   │   │   │   ├── train
+  │   │   │   │   |   │   │   │   ├── xxx.png
+  │   │   │   │   |   │   │   │   ├── ...
+  │   │   │   │   |   │   │   │   └── xxx.png
+  │   │   │   │   │   │   │   ├── test
+  │   │   │   │   |   │   │   │   ├── yyy.png
+  │   │   │   │   |   │   │   │   ├── ...
+  │   │   │   │   |   │   │   │   └── yyy.png
+```
+
+### Training commands
+
+```shell
+mim train mmseg ./configs/${CONFIG_PATH}
+```
+
+To train on multiple GPUs, e.g. 8 GPUs, run the following command:
+
+```shell
+mim train mmseg ./configs/${CONFIG_PATH}  --launcher pytorch --gpus 8
+```
+
+### Testing commands
+
+```shell
+mim test mmseg ./configs/${CONFIG_PATH}  --checkpoint ${CHECKPOINT_PATH}
+```
+
+<!-- List the results as usually done in other model's README. [Example](https://github.com/open-mmlab/mmsegmentation/tree/dev-1.x/configs/fcn#results-and-models)
+
+You should claim whether this is based on the pre-trained weights, which are converted from the official release; or it's a reproduced result obtained from retraining the model in this project. -->
+
+## Results
+
+### ISIC-2016 Task1
+
+|     Method      | Backbone | Crop Size |   lr   | mIoU | mDice |                                                                                             config                                                                                              |
+| :-------------: | :------: | :-------: | :----: | :--: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| fcn_unet_s5-d16 |   unet   |  512x512  |  0.01  |  -   |   -   |  [config](https://github.com/open-mmlab/mmsegmentation/tree/dev-1.x/projects/medical/2d_image/dermoscopy/isic2016_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_isic2016-task1-512x512.py)  |
+| fcn_unet_s5-d16 |   unet   |  512x512  | 0.001  |  -   |   -   | [config](https://github.com/open-mmlab/mmsegmentation/tree/dev-1.x/projects/medical/2d_image/dermoscopy/isic2016_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_isic2016-task1-512x512.py)  |
+| fcn_unet_s5-d16 |   unet   |  512x512  | 0.0001 |  -   |   -   | [config](https://github.com/open-mmlab/mmsegmentation/tree/dev-1.x/projects/medical/2d_image/dermoscopy/isic2016_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_isic2016-task1-512x512.py) |
+
+## Checklist
+
+- [x] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.
+
+  - [x] Finish the code
+
+  - [x] Basic docstrings & proper citation
+
+  - [x] Test-time correctness
+
+  - [x] A full README
+
+- [x] Milestone 2: Indicates a successful model implementation.
+
+  - [x] Training-time correctness
+
+- [ ] Milestone 3: Good to be a part of our core package!
+
+  - [ ] Type hints and docstrings
+
+  - [ ] Unit tests
+
+  - [ ] Code polishing
+
+  - [ ] Metafile.yml
+
+- [ ] Move your modules into the core package following the codebase's file hierarchy structure.
+
+- [ ] Refactor your modules into the core package following the codebase's file hierarchy structure.

projects/medical/2d_image/dermoscopy/isic2016_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_isic2016-task1-512x512.py

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+_base_ = [
+    'mmseg::_base_/models/fcn_unet_s5-d16.py', './isic2016-task1_512x512.py',
+    'mmseg::_base_/default_runtime.py',
+    'mmseg::_base_/schedules/schedule_20k.py'
+]
+custom_imports = dict(imports='datasets.isic2016-task1_dataset')
+img_scale = (512, 512)
+data_preprocessor = dict(size=img_scale)
+optimizer = dict(lr=0.0001)
+optim_wrapper = dict(optimizer=optimizer)
+model = dict(
+    data_preprocessor=data_preprocessor,
+    decode_head=dict(num_classes=2),
+    auxiliary_head=None,
+    test_cfg=dict(mode='whole', _delete_=True))
+vis_backends = None
+visualizer = dict(vis_backends=vis_backends)

projects/medical/2d_image/dermoscopy/isic2016_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_isic2016-task1-512x512.py

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+_base_ = [
+    'mmseg::_base_/models/fcn_unet_s5-d16.py', './isic2016-task1_512x512.py',
+    'mmseg::_base_/default_runtime.py',
+    'mmseg::_base_/schedules/schedule_20k.py'
+]
+custom_imports = dict(imports='datasets.isic2016-task1_dataset')
+img_scale = (512, 512)
+data_preprocessor = dict(size=img_scale)
+optimizer = dict(lr=0.001)
+optim_wrapper = dict(optimizer=optimizer)
+model = dict(
+    data_preprocessor=data_preprocessor,
+    decode_head=dict(num_classes=2),
+    auxiliary_head=None,
+    test_cfg=dict(mode='whole', _delete_=True))
+vis_backends = None
+visualizer = dict(vis_backends=vis_backends)

projects/medical/2d_image/dermoscopy/isic2016_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_isic2016-task1-512x512.py

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+_base_ = [
+    'mmseg::_base_/models/fcn_unet_s5-d16.py', './isic2016-task1_512x512.py',
+    'mmseg::_base_/default_runtime.py',
+    'mmseg::_base_/schedules/schedule_20k.py'
+]
+custom_imports = dict(imports='datasets.isic2016-task1_dataset')
+img_scale = (512, 512)
+data_preprocessor = dict(size=img_scale)
+optimizer = dict(lr=0.01)
+optim_wrapper = dict(optimizer=optimizer)
+model = dict(
+    data_preprocessor=data_preprocessor,
+    decode_head=dict(num_classes=2),
+    auxiliary_head=None,
+    test_cfg=dict(mode='whole', _delete_=True))
+vis_backends = None
+visualizer = dict(vis_backends=vis_backends)

projects/medical/2d_image/dermoscopy/isic2016_task1/configs/isic2016-task1_512x512.py

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+dataset_type = 'ISIC2017Task1'
+data_root = 'data/'
+img_scale = (512, 512)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', scale=img_scale, keep_ratio=False),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='PackSegInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=img_scale, keep_ratio=False),
+    dict(type='LoadAnnotations'),
+    dict(type='PackSegInputs')
+]
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='InfiniteSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='train.txt',
+        data_prefix=dict(img_path='images/', seg_map_path='masks/'),
+        pipeline=train_pipeline))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='test.txt',
+        data_prefix=dict(img_path='images/', seg_map_path='masks/'),
+        pipeline=test_pipeline))
+test_dataloader = val_dataloader
+val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mDice'])
+test_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mDice'])

projects/medical/2d_image/dermoscopy/isic2016_task1/datasets/isic2016-task1_dataset.py

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+from mmseg.datasets import BaseSegDataset
+from mmseg.registry import DATASETS
+
+
+@DATASETS.register_module()
+class ISIC2017Task1(BaseSegDataset):
+    """ISIC2017Task1 dataset.
+
+    In segmentation map annotation for ISIC2017Task1,
+    ``reduce_zero_label`` is fixed to False. The ``img_suffix``
+    is fixed to '.png' and ``seg_map_suffix`` is fixed to '.png'.
+
+    Args:
+        img_suffix (str): Suffix of images. Default: '.png'
+        seg_map_suffix (str): Suffix of segmentation maps. Default: '.png'
+        reduce_zero_label (bool): Whether to mark label zero as ignored.
+            Default to False.
+    """
+    METAINFO = dict(classes=('normal', 'skin lesion'))
+
+    def __init__(self,
+                 img_suffix='.png',
+                 seg_map_suffix='.png',
+                 reduce_zero_label=False,
+                 **kwargs) -> None:
+        super().__init__(
+            img_suffix=img_suffix,
+            seg_map_suffix=seg_map_suffix,
+            reduce_zero_label=reduce_zero_label,
+            **kwargs)

projects/medical/2d_image/dermoscopy/isic2016_task1/tools/prepare_dataset.py

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+import glob
+import os
+import shutil
+
+import numpy as np
+from PIL import Image
+
+
+def check_maskid(train_imgs):
+    for i in train_masks:
+        img = Image.open(i)
+        print(np.unique(np.array(img)))
+
+
+def reformulate_file(image_list, mask_list):
+    file_list = []
+    for idx, (imgp,
+              maskp) in enumerate(zip(sorted(image_list), sorted(mask_list))):
+        item = {'image': imgp, 'label': maskp}
+        file_list.append(item)
+    return file_list
+
+
+def check_file_exist(pair_list):
+    rel_path = os.getcwd()
+    for idx, sample in enumerate(pair_list):
+        image_path = sample['image']
+        assert os.path.exists(os.path.join(rel_path, image_path))
+        if 'label' in sample:
+            mask_path = sample['label']
+            assert os.path.exists(os.path.join(rel_path, mask_path))
+    print('all file path ok!')
+
+
+def convert_maskid(mask):
+    # add mask id conversion
+    arr_mask = np.array(mask).astype(np.uint8)
+    arr_mask[arr_mask == 255] = 1
+    return Image.fromarray(arr_mask)
+
+
+def process_dataset(file_lists, part_dir_dict):
+    for ith, part in enumerate(file_lists):
+        part_dir = part_dir_dict[ith]
+        for sample in part:
+            # read image and mask
+            image_path = sample['image']
+            if 'label' in sample:
+                mask_path = sample['label']
+
+            basename = os.path.basename(image_path)
+            targetname = basename.split('.')[0]  # from image name
+
+            # check image file
+            img_save_path = os.path.join(root_path, 'images', part_dir,
+                                         targetname + save_img_suffix)
+            if not os.path.exists(img_save_path):
+                if not image_path.endswith('.png'):
+                    src = Image.open(image_path)
+                    src.save(img_save_path)
+                else:
+                    shutil.copy(image_path, img_save_path)
+
+            if mask_path is not None:
+                mask_save_path = os.path.join(root_path, 'masks', part_dir,
+                                              targetname + save_seg_map_suffix)
+                if not os.path.exists(mask_save_path):
+                    # check mask file
+                    mask = Image.open(mask_path).convert('L')
+                    # convert mask id
+                    mask = convert_maskid(mask)
+                    if not mask_path.endswith('.png'):
+                        mask.save(mask_save_path)
+                    else:
+                        mask.save(mask_save_path)
+
+        # print image num
+        part_dir_folder = os.path.join(root_path, 'images', part_dir)
+        print(
+            f'{part_dir} has {len(os.listdir(part_dir_folder))} images completed!'  # noqa
+        )
+
+
+if __name__ == '__main__':
+
+    root_path = 'data/'  # original file
+    img_suffix = '.jpg'
+    seg_map_suffix = '.png'
+    save_img_suffix = '.png'
+    save_seg_map_suffix = '.png'
+
+    train_imgs = glob.glob('data/ISBI2016_ISIC_Part1_Training_Data/*'  # noqa
+                           + img_suffix)
+    train_masks = glob.glob(
+        'data/ISBI2016_ISIC_Part1_Training_GroundTruth/*'  # noqa
+        + seg_map_suffix)
+
+    test_imgs = glob.glob('data/ISBI2016_ISIC_Part1_Test_Data/*' + img_suffix)
+    test_masks = glob.glob(
+        'data/ISBI2016_ISIC_Part1_Test_GroundTruth/*'  # noqa
+        + seg_map_suffix)
+
+    assert len(train_imgs) == len(train_masks)
+    assert len(test_imgs) == len(test_masks)
+
+    print(f'training images: {len(train_imgs)}, test images: {len(test_imgs)}')
+
+    os.system('mkdir -p ' + root_path + 'images/train/')
+    os.system('mkdir -p ' + root_path + 'images/test/')
+    os.system('mkdir -p ' + root_path + 'masks/train/')
+    os.system('mkdir -p ' + root_path + 'masks/test/')
+
+    train_pair_list = reformulate_file(train_imgs, train_masks)
+    test_pair_list = reformulate_file(test_imgs, test_masks)
+
+    check_file_exist(train_pair_list)
+    check_file_exist(test_pair_list)
+
+    part_dir_dict = {0: 'train/', 1: 'test/'}
+    process_dataset([train_pair_list, test_pair_list], part_dir_dict)