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

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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

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Keras Image Embeddings using Contrastive Loss

Code for reproducing experiments in "Improved Training of Wasserstein GANs"

Code for the paper Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations (AKBC 2021).

AI grand challenge 2020 Repo (Speech Recognition Track)

A crash course in six episodes for software developers who want to become machine learning practitioners.

COPA-SSE contains crowdsourced explanations for the Balanced COPA dataset

PyTorch implementation of the paper:A Convolutional Approach to Melody Line Identification in Symbolic Scores.

Build fully-functioning computer vision models with PyTorch

Hardware accelerated, batchable and differentiable optimizers in JAX.

Neural Radiance Fields Using PyTorch

Texture mapping with variational auto-encoders

TJU Deep Learning & Neural Network

CTF challenges from redpwnCTF 2021

Face uncertainty quantification or estimation using PyTorch.

Python PID Tuner - Makes a model of the System from a Process Reaction Curve and calculates PID Gains

A simple baseline for 3d human pose estimation in tensorflow. Presented at ICCV 17.

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