[Project] Medical semantic seg dataset: ISIC-2017 Task1 (#2709)

projects/medical/2d_image/dermoscopy/isic2017_task1/README.md

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+# ISIC-2017 Task1
+
+## Description
+
+This project support **`ISIC-2017 Task1 `**, and the dataset used in this project can be downloaded from [here](https://challenge.isic-archive.com/data/#2017).
+
+### Dataset Overview
+
+The goal of the challenge is to help participants develop image analysis tools to enable the automated diagnosis of melanoma from dermoscopic images.
+
+This challenge provides training data (~2000 images) for participants to engage in all 3 components of lesion image analysis. A separate public validation dataset (~150 images) and blind held-out test dataset (~600 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-2017 Task1](https://challenge.isic-archive.com/data/#2017) | full body         | segmentation | dermoscopy | 2            | 2000/150/600          | yes/yes/yes            | 2017         | [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 |
+| :---------: | :--------: | :--------: | :------: | :------: | :-------: | :-------: |
+|   normal    |    2000    |   82.86    |   150    |  73.88   |    600    |   70.62   |
+| skin lesion |    2000    |   17.14    |   150    |  26.12   |    600    |   29.38   |
+
+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/isic2017_task1/isic2017_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 isic2017_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/#2017) 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
+  │   │   │   │   ├── isic2017_task1
+  │   │   │   │   │   ├── configs
+  │   │   │   │   │   ├── datasets
+  │   │   │   │   │   ├── tools
+  │   │   │   │   │   ├── data
+  │   │   │   │   │   │   ├── train.txt
+  │   │   │   │   │   │   ├── val.txt
+  │   │   │   │   │   │   ├── test.txt
+  │   │   │   │   │   │   ├── images
+  │   │   │   │   │   │   │   ├── train
+  │   │   │   │   |   │   │   │   ├── xxx.png
+  │   │   │   │   |   │   │   │   ├── ...
+  │   │   │   │   |   │   │   │   └── xxx.png
+  │   │   │   │   │   │   │   ├── val
+  │   │   │   │   |   │   │   │   ├── yyy.png
+  │   │   │   │   |   │   │   │   ├── ...
+  │   │   │   │   |   │   │   │   └── yyy.png
+  │   │   │   │   │   │   │   ├── test
+  │   │   │   │   |   │   │   │   ├── yyy.png
+  │   │   │   │   |   │   │   │   ├── ...
+  │   │   │   │   |   │   │   │   └── yyy.png
+  │   │   │   │   │   │   ├── masks
+  │   │   │   │   │   │   │   ├── train
+  │   │   │   │   |   │   │   │   ├── xxx.png
+  │   │   │   │   |   │   │   │   ├── ...
+  │   │   │   │   |   │   │   │   └── xxx.png
+  │   │   │   │   │   │   │   ├── val
+  │   │   │   │   |   │   │   │   ├── yyy.png
+  │   │   │   │   |   │   │   │   ├── ...
+  │   │   │   │   |   │   │   │   └── yyy.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-2017 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/isic2017_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_isic2017-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/isic2017_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_isic2017-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/isic2017_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_isic2017-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
+
+  - [ ] Test-time correctness
+
+  - [x] A full README
+
+- [ ] Milestone 2: Indicates a successful model implementation.
+
+  - [ ] 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/isic2017_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_isic2017-task1-512x512.py

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+_base_ = [
+    'mmseg::_base_/models/fcn_unet_s5-d16.py', './isic2017-task1_512x512.py',
+    'mmseg::_base_/default_runtime.py',
+    'mmseg::_base_/schedules/schedule_20k.py'
+]
+custom_imports = dict(imports='datasets.isic2017-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/isic2017_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_isic2017-task1-512x512.py

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+_base_ = [
+    'mmseg::_base_/models/fcn_unet_s5-d16.py', './isic2017-task1_512x512.py',
+    'mmseg::_base_/default_runtime.py',
+    'mmseg::_base_/schedules/schedule_20k.py'
+]
+custom_imports = dict(imports='datasets.isic2017-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/isic2017_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_isic2017-task1-512x512.py

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+_base_ = [
+    'mmseg::_base_/models/fcn_unet_s5-d16.py', './isic2017-task1_512x512.py',
+    'mmseg::_base_/default_runtime.py',
+    'mmseg::_base_/schedules/schedule_20k.py'
+]
+custom_imports = dict(imports='datasets.isic2017-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/isic2017_task1/configs/isic2017-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,
+        data_prefix=dict(
+            img_path='images/train/', seg_map_path='masks/train/'),
+        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,
+        data_prefix=dict(img_path='images/val/', seg_map_path='masks/val/'),
+        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/isic2017_task1/datasets/isic2017-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)