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

Last update: Dec 09, 2022

Related tags

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

Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.

Minimal PyTorch implementation of Generative Latent Optimization from the paper "Optimizing the Latent Space of Generative Networks"

A state of the art of new lightweight YOLO model implemented by TensorFlow 2.

Code and dataset for ACL2018 paper "Exploiting Document Knowledge for Aspect-level Sentiment Classification"

Implementation of EMNLP 2017 Paper "Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog" using PyTorch and ParlAI

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PyTorch implementation of NeurIPS 2021 paper: "CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration"

Weakly supervised medical named entity classification

DeepLab2: A TensorFlow Library for Deep Labeling

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