DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

Last update: Dec 09, 2022

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Deep Learning DCSAU-Net

Overview

DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

By Qing Xu, Wenting Duan and Na He

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

Acknowledgement

The codes are modified from ResNeSt, U-Net

Owner

Qing Xu

GitHub Repository

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DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

Related tags

Overview

DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

Requirements

Dataset

CSV generation

Train

Evaluation

Acknowledgement

Owner

Qing Xu

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