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https://github.com/bwconrad/style-match-mitosis-detection
Style Match: Reducing the Scanner Induced Domain Gap in Mitosis Detection using Style Transfer Alignment
https://github.com/bwconrad/style-match-mitosis-detection
Last synced: about 1 month ago
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Style Match: Reducing the Scanner Induced Domain Gap in Mitosis Detection using Style Transfer Alignment
- Host: GitHub
- URL: https://github.com/bwconrad/style-match-mitosis-detection
- Owner: bwconrad
- License: gpl-3.0
- Created: 2021-05-24T13:43:05.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2024-04-04T03:14:21.000Z (9 months ago)
- Last Synced: 2024-05-15T09:46:49.531Z (8 months ago)
- Language: Python
- Homepage:
- Size: 29.1 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Style Match for Mitotic Figure Detection
Repository for "[Style Match: Reducing the Scanner Induced Domain Gap in Mitosis Detection using Style Transfer Alignment](assets/style_match_conrad22.pdf)".
## Setup
### Requirements
- Python >= 3.8
- `pip install -r requirements.txt`
### Data
- [MIDOG 2021 dataset](https://zenodo.org/record/4643381).
- To apply stain normalization to the dataset run `python scripts/stain_normalization -i data/midog -o data/normalizaed.`
- Optionally for STRAP, [WikiArt](https://github.com/cs-chan/ArtGAN/tree/master/WikiArt%20Dataset) and [COCO 2014 train](https://cocodataset.org/#download) datasets.
## Usage
- __Pretrained weights can be downloaded [here](https://drive.google.com/drive/folders/1t4e3AMhkeabucJ49arcuQSB80zOdmi0R?usp=sharing).__
### Style Transfer Demo Notebook
- `notebooks/style_transfer_inference.ipynb` provides a demo of the style transfer model applied to WSIs.
- To run it, download the [ pretrained weights ](https://drive.google.com/drive/folders/1t4e3AMhkeabucJ49arcuQSB80zOdmi0R?usp=sharing) and [ MIDOG dataset ](https://zenodo.org/record/4643381).
### Style Transfer
- __Train__:
- Content images from scanner 1 and style images from scanner 4:
```
python train_style_transfer_midog.py --gpus 1 --precision 16 --max_steps 80000 \
--data.batch_size 16 --data.data_path data/midog --data.content_scanners 1 \
--data.style_scanners 4 --model.use_bfg True --model.use_skip True \
--check_val_every_n_epoch 100
```
- Content and style images from all scanners:
```
python train_style_transfer_midog.py --gpus 1 --precision 16 --max_steps 80000 \
--data.batch_size 16 --data.data_path data/midog --data.content_scanners "[1,2,3,4]" \
--data.style_scanners "[1,2,3,4]" --model.use_bfg True --model.use_skip True \
--check_val_every_n_epoch 100
```
- Content images from COCO and style images from WikiArt:
```
python train_style_transfer.py --gpus 1 --precision 16 --max_steps 80000 \
--data.batch_size 16 --data.resize_size 512 --data.content_path data/coco/ \
--data.style_path data/wikiart/ --model.use_skip True --model.use_bfg True \
--val_check_interval 5000
```
- __Evaluation__:
- SSIM for model trained with content images from scanner 1 and style images from scanner 4:
```
python test_style_transfer_midog.py --gpus 1 --precision 16 \
--data.content_scanners 1 --data.style_scanners 4 \
--checkpoint weights/adain_bfg_skip_c1_s4.ckpt
```
### Classification
- __Train__:
- Scanner classification:
```
python train_classifier.py --gpus 1 --precision 16 --max_epochs 5 \
--data.data_path data/midog
```
- __Evaluation__:
- On validation set:
```
python test_classifier.py --gpus 1 --precision 16 --data.data_path data/midog \
--checkpoint weights/classifier.ckpt
```
- On validation set augmented by style transfer model:
```
python test_classifier.py --gpus 1 --precision 16 --data.data_path data/midog \
--data.style_scanner 4 --model.style_checkpoint weights/adain_bfg_skip_c1_s4.ckpt \
--checkpoint weights/classifier.ckpt
```
### Detection
- __Train__:
- Standard:
```
python train_detector.py --gpus 1 --precision 16 --max_epochs 100 --model.schedule step \
--model.steps "[50]" --data.data_path data/midog --data.ann_path data/MIDOG.json \
--data.train_scanners 1 --data.val_scanners 1
```
- Style Match:
```
python train_detector.py --gpus 1 --precision 16 --max_epochs 100 --model.schedule step \
--model.steps "[50]" --data.data_path data/midog --data.ann_path data/MIDOG.json \
--data.train_scanners 1 --data.val_scanners 1 --data.style_scanner 2 \
--model.style_checkpoint weights/all_scanners.ckpt
```
- FDA:
```
python train_detector.py --gpus 1 --precision 16 --max_epochs 100 --model.schedule step \
--model.steps "[50]" --data.data_path data/midog --data.ann_path data/MIDOG.json \
--data.train_scanners 1 --data.val_scanners 1 --data.style_scanner 2 --data.fda_beta 0.01
```
- STRAP:
```
python train_detector.py --gpus 1 --precision 16 --max_epochs 100 --model.schedule step \
--model.steps "[50]" --data.data_path data/midog --data.ann_path data/MIDOG.json \
--data.random_style_path data/wikiart/ --data.train_scanners 1 --data.val_scanners 1 \
--model.style_checkpoint weights/random_style.ckpt
```
- Stain Normalization:
- Apply stain normalization to the MIDOG dataset set by first running `python scripts/stain_normalization -i data/midog -o data/normalizaed`.
```
python train_detector.py --gpus 1 --precision 16 --max_epochs 100 --model.schedule step \
--model.steps "[50]" --data.data_path data/normalized --data.ann_path data/MIDOG.json \
--data.train_scanners 1 --data.val_scanners 1
```
- __Evaluation__:
- Standard:
```
python test_detector.py --gpus 1 --precision 16 --data.data_path data/midog/ \
--data.ann_path data/MIDOG.json --checkpoint weights/reg_s1.ckpt --data.test_scanners 3 \
--model.eval_only_positives true
```
- Style Match:
```
python test_detector.py --gpus 1 --precision 16 --data.data_path data/midog/ \
--data.ann_path data/MIDOG.json --checkpoint weights/st_s1.ckpt --data.test_scanners 3 \
--model.eval_only_positives true --data.style_scanners 2 \
--model.style_checkpoint weights/all_scanners.ckpt
```
- FDA:
```
python test_detector.py --gpus 1 --precision 16 --data.data_path data/midog/ \
--data.ann_path data/MIDOG.json --checkpoint weights/fda_s1.ckpt --data.test_scanners 3 \
--model.eval_only_positives true --data.style_scanners 2 --data.fda_beta 0.01 \
--data.workers 0
```
- STRAP:
```
python test_detector.py --gpus 1 --precision 16 --data.data_path data/midog/ \
--data.ann_path data/MIDOG.json --data.random_style_path data/wikiart/ \
--checkpoint weights/rand_s1.ckpt --data.test_scanners 3 --model.eval_only_positives true \
--model.style_checkpoint weights/random_style.ckpt
```
- Stain Normalization:
```
python test_detector.py --gpus 1 --precision 16 --data.data_path data/normalized/ \
--data.ann_path data/MIDOG.json --checkpoint weights/norm_s1.ckpt --data.test_scanners 3 \
--model.eval_only_positives true --data.style_scanners 2 \
--model.style_checkpoint weights/all_scanners.ckpt
```