open-mmlab · xiexinch · Jun 22, 2023 · Mar 5, 2023 · Mar 6, 2023 · Mar 6, 2023
diff --git a/projects/medical/2d_image/dermoscopy/isic2017_task1/README.md b/projects/medical/2d_image/dermoscopy/isic2017_task1/README.md
@@ -0,0 +1,158 @@
+# 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:/open-mmlab/mim) v0.3.4
+- [MMCV](https:/open-mmlab/mmcv) v2.0.0rc4
+- [MMEngine](https:/open-mmlab/mmengine) v0.2.0 or higher
+- [MMSegmentation](https:/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:/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:/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:/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:/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.
diff --git a/...py/isic2017_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_isic2017-task1-512x512.py b/...py/isic2017_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_isic2017-task1-512x512.py
@@ -0,0 +1,17 @@
+_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)
diff --git a/...opy/isic2017_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_isic2017-task1-512x512.py b/...opy/isic2017_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_isic2017-task1-512x512.py
@@ -0,0 +1,17 @@
+_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)
diff --git a/...copy/isic2017_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_isic2017-task1-512x512.py b/...copy/isic2017_task1/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_isic2017-task1-512x512.py
@@ -0,0 +1,17 @@
+_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)
diff --git a/projects/medical/2d_image/dermoscopy/isic2017_task1/configs/isic2017-task1_512x512.py b/projects/medical/2d_image/dermoscopy/isic2017_task1/configs/isic2017-task1_512x512.py
@@ -0,0 +1,41 @@
+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'])
diff --git a/projects/medical/2d_image/dermoscopy/isic2017_task1/datasets/isic2017-task1_dataset.py b/projects/medical/2d_image/dermoscopy/isic2017_task1/datasets/isic2017-task1_dataset.py
@@ -0,0 +1,30 @@
+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)