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[Project] Medical semantic seg dataset: Covid 19 ct cxr (#2688)
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projects/medical/2d_image/x_ray/covid_19_ct_cxr/README.md
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projects/medical/2d_image/x_ray/covid_19_ct_cxr/README.md
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# Covid-19 CT Chest X-ray Dataset
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## Description
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This project supports **`Covid-19 CT Chest X-ray Dataset`**, which can be downloaded from [here](https://github.com/ieee8023/covid-chestxray-dataset).
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### Dataset Overview
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In the context of a COVID-19 pandemic, we want to improve prognostic predictions to triage and manage patient care. Data is the first step to developing any diagnostic/prognostic tool. While there exist large public datasets of more typical chest X-rays from the NIH \[Wang 2017\], Spain \[Bustos 2019\], Stanford \[Irvin 2019\], MIT \[Johnson 2019\] and Indiana University \[Demner-Fushman 2016\], there is no collection of COVID-19 chest X-rays or CT scans designed to be used for computational analysis.
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The 2019 novel coronavirus (COVID-19) presents several unique features [Fang, 2020](https://pubs.rsna.org/doi/10.1148/radiol.2020200432) and [Ai 2020](https://pubs.rsna.org/doi/10.1148/radiol.2020200642). While the diagnosis is confirmed using polymerase chain reaction (PCR), infected patients with pneumonia may present on chest X-ray and computed tomography (CT) images with a pattern that is only moderately characteristic for the human eye [Ng, 2020](https://pubs.rsna.org/doi/10.1148/ryct.2020200034). In late January, a Chinese team published a paper detailing the clinical and paraclinical features of COVID-19. They reported that patients present abnormalities in chest CT images with most having bilateral involvement [Huang 2020](<https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30183-5/fulltext>). Bilateral multiple lobular and subsegmental areas of consolidation constitute the typical findings in chest CT images of intensive care unit (ICU) patients on admission [Huang 2020](<https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30183-5/fulltext>). In comparison, non-ICU patients show bilateral ground-glass opacity and subsegmental areas of consolidation in their chest CT images [Huang 2020](<https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30183-5/fulltext>). In these patients, later chest CT images display bilateral ground-glass opacity with resolved consolidation [Huang 2020](<https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30183-5/fulltext>).
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### Statistic Information
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| Dataset Name | Anatomical Region | Task Type | Modality | Nnum. Classes | Train/Val/Test Images | Train/Val/Test Labeled | Release date | License |
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| ---------------------------------------------------------------------- | ----------------- | ------------ | -------- | ------------- | --------------------- | ---------------------- | ------------ | --------------------------------------------------------------------- |
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| [Covid-19-ct-cxr](https://github.com/ieee8023/covid-chestxray-dataset) | thorax | segmentation | x_ray | 2 | 205/-/714 | yes/-/no | 2021 | [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
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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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| background | 205 | 72.84 | - | - | - | - |
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| lung | 205 | 27.16 | - | - | - | - |
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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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### Dataset Citation
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```
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@article{cohen2020covidProspective,
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title={{COVID-19} Image Data Collection: Prospective Predictions Are the Future},
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author={Joseph Paul Cohen and Paul Morrison and Lan Dao and Karsten Roth and Tim Q Duong and Marzyeh Ghassemi},
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journal={arXiv 2006.11988},
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year={2020}
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}
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@article{cohen2020covid,
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title={COVID-19 image data collection},
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author={Joseph Paul Cohen and Paul Morrison and Lan Dao},
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journal={arXiv 2003.11597},
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year={2020}
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}
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```
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### Prerequisites
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- Python v3.8
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- PyTorch v1.10.0
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- pillow(PIL) v9.3.0 9.3.0
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- scikit-learn(sklearn) v1.2.0 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 `covid_19_ct_cxr/` 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://github.com/ieee8023/covid-chestxray-dataset) and decompress data to path `'data/'`.
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- run script `"python tools/prepare_dataset.py"` to format data 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`, `val.txt` and `test.txt`. If the label of official validation set and test set cannot be obtained, we generate `train.txt` and `val.txt` from the training set randomly.
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```shell
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mkdir data && cd data
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git clone git@github.com:ieee8023/covid-chestxray-dataset.git
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cd ..
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python tools/prepare_dataset.py
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python ../../tools/split_seg_dataset.py
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```
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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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│ │ │ ├── x_ray
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│ │ │ │ ├── covid_19_ct_cxr
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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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│ │ │ │ │ │ ├── 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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│ │ │ │ │ │ ├── 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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```
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### Divided Dataset Information
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***Note: The table information below is divided by ourselves.***
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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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| background | 164 | 72.88 | 41 | 72.69 | - | - |
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| lung | 164 | 27.12 | 41 | 27.31 | - | - |
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### Training commands
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To train models on a single server with one GPU. (default)
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```shell
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mim train mmseg ./configs/${CONFIG_FILE}
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```
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### Testing commands
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To test models on a single server with one GPU. (default)
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```shell
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mim test mmseg ./configs/${CONFIG_FILE} --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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## 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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- [x] Test-time correctness
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- [x] A full README
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- [x] Milestone 2: Indicates a successful model implementation.
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- [x] 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.
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dataset_type = 'Covid19CXRDataset'
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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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ann_file='train.txt',
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data_prefix=dict(img_path='images/', seg_map_path='masks/'),
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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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ann_file='val.txt',
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data_prefix=dict(img_path='images/', seg_map_path='masks/'),
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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'])
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_base_ = [
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'mmseg::_base_/models/fcn_unet_s5-d16.py', './covid-19-ct-cxr_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.covid-19-ct-cxr_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(
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num_classes=2, loss_decode=dict(use_sigmoid=True), out_channels=1),
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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)
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_base_ = [
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'mmseg::_base_/models/fcn_unet_s5-d16.py', './covid-19-ct-cxr_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.covid-19-ct-cxr_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)
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_base_ = [
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'mmseg::_base_/models/fcn_unet_s5-d16.py', './covid-19-ct-cxr_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.covid-19-ct-cxr_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)
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_base_ = [
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'mmseg::_base_/models/fcn_unet_s5-d16.py', './covid-19-ct-cxr_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.covid-19-ct-cxr_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)
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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 Covid19CXRDataset(BaseSegDataset):
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"""Covid19CXRDataset dataset.
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In segmentation map annotation for Covid19CXRDataset,
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0 stands for background, which is included in 2 categories.
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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=('background', 'lung'))
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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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import os
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import numpy as np
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from PIL import Image
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root_path = 'data/'
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src_img_dir = os.path.join(root_path, 'covid-chestxray-dataset', 'images')
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src_mask_dir = os.path.join(root_path, 'covid-chestxray-dataset',
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'annotations/lungVAE-masks')
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tgt_img_train_dir = os.path.join(root_path, 'images/train/')
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tgt_mask_train_dir = os.path.join(root_path, 'masks/train/')
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tgt_img_test_dir = os.path.join(root_path, 'images/test/')
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os.system('mkdir -p ' + tgt_img_train_dir)
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os.system('mkdir -p ' + tgt_mask_train_dir)
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os.system('mkdir -p ' + tgt_img_test_dir)
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def convert_label(img, convert_dict):
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arr = np.zeros_like(img, dtype=np.uint8)
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for c, i in convert_dict.items():
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arr[img == c] = i
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return arr
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if __name__ == '__main__':
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all_img_names = os.listdir(src_img_dir)
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all_mask_names = os.listdir(src_mask_dir)
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for img_name in all_img_names:
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base_name = img_name.replace('.png', '')
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base_name = base_name.replace('.jpg', '')
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base_name = base_name.replace('.jpeg', '')
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mask_name_orig = base_name + '_mask.png'
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if mask_name_orig in all_mask_names:
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mask_name = base_name + '.png'
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src_img_path = os.path.join(src_img_dir, img_name)
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src_mask_path = os.path.join(src_mask_dir, mask_name_orig)
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tgt_img_path = os.path.join(tgt_img_train_dir, img_name)
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tgt_mask_path = os.path.join(tgt_mask_train_dir, mask_name)
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img = Image.open(src_img_path).convert('RGB')
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img.save(tgt_img_path)
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mask = np.array(Image.open(src_mask_path))
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mask = convert_label(mask, {0: 0, 255: 1})
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mask = Image.fromarray(mask)
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mask.save(tgt_mask_path)
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else:
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src_img_path = os.path.join(src_img_dir, img_name)
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tgt_img_path = os.path.join(tgt_img_test_dir, img_name)
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img = Image.open(src_img_path).convert('RGB')
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img.save(tgt_img_path)
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