# 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} ``` ## 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.