DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation
December 5, 2023 · View on GitHub
News
2022.08.25: The DCSAU-Net model has been optimised. The paper will be updated later.
2022.09.27: The updated preprint has been available at arXiv.
2022.10.05: The method of calculating FLOPs, parameters and FPS has been uploaded.
2022.12.09: A requirements.txt for Linux environment has been uploaded.
2023.02.02: The article has been accepted and available in the journal: Computers in Biology and Medicine.
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Requirements
- pytorch==1.10.0
- pytorch-lightning==1.1.0
- albumentations==0.3.2
- seaborn
- sklearn
Dataset
To apply the model on a custom dataset, the data tree should be constructed as:
├── data
├── images
├── image_1.png
├── image_2.png
├── image_n.png
├── masks
├── image_1.png
├── image_2.png
├── image_n.png
CSV generation
python data_split_csv.py --dataset your/data/path --size 0.9
Train
python train.py --dataset your/data/path --csvfile your/csv/path --loss dice --batch 16 --lr 0.001 --epoch 150
Evaluation
python eval_binary.py --dataset your/data/path --csvfile your/csv/path --model save_models/epoch_last.pth --debug True