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[Project] Medical semantic seg dataset: ISIC-2017 Task1 (open-mmlab#2709)
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‎projects/medical/2d_image/dermoscopy/isic2017_task1/README.md‎

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# ISIC-2017 Task1
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## Description
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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).
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### Dataset Overview
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The goal of the challenge is to help participants develop image analysis tools to enable the automated diagnosis of melanoma from dermoscopic images.
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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.
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### Original Statistic Information
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| Dataset name | Anatomical region | Task type | Modality | Num. Classes | Train/Val/Test Images | Train/Val/Test Labeled | Release Date | License |
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| ---------------------------------------------------------------- | ----------------- | ------------ | ---------- | ------------ | --------------------- | ---------------------- | ------------ | ---------------------------------------------------------------------- |
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| [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/) |
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| Class Name | Num. Train | Pct. Train | Num. Val | Pct. Val | Num. Test | Pct. Test |
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| :---------: | :--------: | :--------: | :------: | :------: | :-------: | :-------: |
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| normal | 2000 | 82.86 | 150 | 73.88 | 600 | 70.62 |
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| skin lesion | 2000 | 17.14 | 150 | 26.12 | 600 | 29.38 |
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Note:
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- `Pct` means percentage of pixels in this category in all pixels.
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### Visualization
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![bac](https://raw.githubusercontent.com/uni-medical/medical-datasets-visualization/main/2d/semantic_seg/dermoscopy/isic2017_task1/isic2017_task1.png)
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### Prerequisites
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- Python 3.8
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- PyTorch 1.10.0
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- pillow(PIL) 9.3.0
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- scikit-learn(sklearn) 1.2.0
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- [MIM](https://github.com/open-mmlab/mim) v0.3.4
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- [MMCV](https://github.com/open-mmlab/mmcv) v2.0.0rc4
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- [MMEngine](https://github.com/open-mmlab/mmengine) v0.2.0 or higher
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- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation) v1.0.0rc5
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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:
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```shell
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export PYTHONPATH=`pwd`:$PYTHONPATH
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```
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### Dataset preparing
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- download dataset from [here](https://challenge.isic-archive.com/data/#2017) and decompression data to path 'data/'.
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- run script `"python tools/prepare_dataset.py"` to split dataset and change folder structure as below.
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- 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.
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```none
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mmsegmentation
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├── mmseg
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├── projects
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│ ├── medical
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│ │ ├── 2d_image
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│ │ │ ├── dermoscopy
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│ │ │ │ ├── isic2017_task1
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│ │ │ │ │ ├── configs
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│ │ │ │ │ ├── datasets
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│ │ │ │ │ ├── tools
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│ │ │ │ │ ├── data
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│ │ │ │ │ │ ├── train.txt
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│ │ │ │ │ │ ├── val.txt
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│ │ │ │ │ │ ├── test.txt
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│ │ │ │ │ │ ├── images
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│ │ │ │ │ │ │ ├── train
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│ │ │ │ | │ │ │ ├── xxx.png
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│ │ │ │ | │ │ │ ├── ...
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│ │ │ │ | │   │   │ └── xxx.png
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│ │ │ │ │ │ │ ├── val
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│ │ │ │ | │ │ │ ├── yyy.png
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│ │ │ │ | │ │ │ ├── ...
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│ │ │ │ | │   │   │ └── yyy.png
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│ │ │ │ │ │ │ ├── test
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│ │ │ │ | │ │ │ ├── yyy.png
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│ │ │ │ | │ │ │ ├── ...
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│ │ │ │ | │   │   │ └── yyy.png
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│ │ │ │ │ │ ├── masks
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│ │ │ │ │ │ │ ├── train
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│ │ │ │ | │ │ │ ├── xxx.png
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│ │ │ │ | │ │ │ ├── ...
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│ │ │ │ | │   │   │ └── xxx.png
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│ │ │ │ │ │ │ ├── val
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│ │ │ │ | │ │ │ ├── yyy.png
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│ │ │ │ | │ │ │ ├── ...
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│ │ │ │ | │   │   │ └── yyy.png
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│ │ │ │ │ │ │ ├── test
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│ │ │ │ | │ │ │ ├── yyy.png
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│ │ │ │ | │ │ │ ├── ...
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│ │ │ │ | │   │   │ └── yyy.png
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```
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### Training commands
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```shell
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mim train mmseg ./configs/${CONFIG_PATH}
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```
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To train on multiple GPUs, e.g. 8 GPUs, run the following command:
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```shell
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mim train mmseg ./configs/${CONFIG_PATH} --launcher pytorch --gpus 8
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```
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### Testing commands
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```shell
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mim test mmseg ./configs/${CONFIG_PATH} --checkpoint ${CHECKPOINT_PATH}
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```
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<!-- 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)
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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. -->
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## Results
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### ISIC-2017 Task1
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| Method | Backbone | Crop Size | lr | mIoU | mDice | config |
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| :-------------: | :------: | :-------: | :----: | :--: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| 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) |
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| 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) |
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| 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) |
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## Checklist
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- [x] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.
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- [x] Finish the code
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- [x] Basic docstrings & proper citation
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- [ ] Test-time correctness
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- [x] A full README
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- [ ] Milestone 2: Indicates a successful model implementation.
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- [ ] Training-time correctness
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- [ ] Milestone 3: Good to be a part of our core package!
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- [ ] Type hints and docstrings
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- [ ] Unit tests
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- [ ] Code polishing
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- [ ] Metafile.yml
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- [ ] Move your modules into the core package following the codebase's file hierarchy structure.
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- [ ] 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_ = [
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'mmseg::_base_/models/fcn_unet_s5-d16.py', './isic2017-task1_512x512.py',
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'mmseg::_base_/default_runtime.py',
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'mmseg::_base_/schedules/schedule_20k.py'
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]
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custom_imports = dict(imports='datasets.isic2017-task1_dataset')
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img_scale = (512, 512)
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data_preprocessor = dict(size=img_scale)
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optimizer = dict(lr=0.0001)
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optim_wrapper = dict(optimizer=optimizer)
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model = dict(
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data_preprocessor=data_preprocessor,
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decode_head=dict(num_classes=2),
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auxiliary_head=None,
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test_cfg=dict(mode='whole', _delete_=True))
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vis_backends = None
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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_ = [
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'mmseg::_base_/models/fcn_unet_s5-d16.py', './isic2017-task1_512x512.py',
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'mmseg::_base_/default_runtime.py',
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'mmseg::_base_/schedules/schedule_20k.py'
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]
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custom_imports = dict(imports='datasets.isic2017-task1_dataset')
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img_scale = (512, 512)
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data_preprocessor = dict(size=img_scale)
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optimizer = dict(lr=0.001)
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optim_wrapper = dict(optimizer=optimizer)
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model = dict(
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data_preprocessor=data_preprocessor,
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decode_head=dict(num_classes=2),
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auxiliary_head=None,
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test_cfg=dict(mode='whole', _delete_=True))
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vis_backends = None
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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_ = [
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'mmseg::_base_/models/fcn_unet_s5-d16.py', './isic2017-task1_512x512.py',
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'mmseg::_base_/default_runtime.py',
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'mmseg::_base_/schedules/schedule_20k.py'
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]
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custom_imports = dict(imports='datasets.isic2017-task1_dataset')
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img_scale = (512, 512)
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data_preprocessor = dict(size=img_scale)
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optimizer = dict(lr=0.01)
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optim_wrapper = dict(optimizer=optimizer)
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model = dict(
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data_preprocessor=data_preprocessor,
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decode_head=dict(num_classes=2),
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auxiliary_head=None,
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test_cfg=dict(mode='whole', _delete_=True))
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vis_backends = None
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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'
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data_root = 'data/'
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img_scale = (512, 512)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', scale=img_scale, keep_ratio=False),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='PackSegInputs')
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='Resize', scale=img_scale, keep_ratio=False),
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dict(type='LoadAnnotations'),
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dict(type='PackSegInputs')
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]
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train_dataloader = dict(
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batch_size=16,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='InfiniteSampler', shuffle=True),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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data_prefix=dict(
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img_path='images/train/', seg_map_path='masks/train/'),
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pipeline=train_pipeline))
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val_dataloader = dict(
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batch_size=1,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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data_prefix=dict(img_path='images/val/', seg_map_path='masks/val/'),
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pipeline=test_pipeline))
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test_dataloader = val_dataloader
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val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mDice'])
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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
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from mmseg.registry import DATASETS
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@DATASETS.register_module()
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class ISIC2017Task1(BaseSegDataset):
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"""ISIC2017Task1 dataset.
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In segmentation map annotation for ISIC2017Task1,
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``reduce_zero_label`` is fixed to False. The ``img_suffix``
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is fixed to '.png' and ``seg_map_suffix`` is fixed to '.png'.
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Args:
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img_suffix (str): Suffix of images. Default: '.png'
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seg_map_suffix (str): Suffix of segmentation maps. Default: '.png'
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reduce_zero_label (bool): Whether to mark label zero as ignored.
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Default to False.
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"""
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METAINFO = dict(classes=('normal', 'skin lesion'))
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def __init__(self,
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img_suffix='.png',
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seg_map_suffix='.png',
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reduce_zero_label=False,
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**kwargs) -> None:
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super().__init__(
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img_suffix=img_suffix,
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seg_map_suffix=seg_map_suffix,
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reduce_zero_label=reduce_zero_label,
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**kwargs)

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