ISBI 2022: Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image.

Related tags

Deep Learning CLCC-semi
Overview

Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image

Introduction

This repository contains the PyTorch implementation of:

Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image, ISBI 2022.

test1

Requirements

  • torch
  • torchvision
  • tqdm
  • albumentations
  • tensorboardX
  • cv2
  • numpy

Usage

1.Folder Structure

We except the data folder has these structures:

root/    
|-- data/
    |-- ISIC/
    |   |-- TrainDataset/
    |   |   |-- images/
    |   |   |   |-- ISIC_0000001.jpg 
    |   |   |   |-- ISIC_0000002.jpg 
    |   |   |   ...
    |   |   |-- masks/
    |   |       |-- ISIC_0000001.jpg 
    |   |       |-- ISIC_0000002.jpg 
    |   |       ...
    |   |-- ValidationDataset/
    |   |   |-- images/
    |   |   |   |-- ISIC_0000003.jpg 
    |   |   |   |-- ISIC_0000004.jpg 
    |   |   |   ...
    |   |   |-- masks/
    |   |       |-- ISIC_0000003.jpg 
    |   |       |-- ISIC_0000004.jpg 
    |   |       ...
    |   |-- TestDataset/
    |       |-- images/
    |       |   |-- ISIC_0000005.jpg 
    |       |   |-- ISIC_0000006.jpg 
    |       |   ...
    |       |-- masks/
    |           |-- ISIC_0000005.jpg 
    |           |-- ISIC_0000006.jpg 
    |           ...
    |-- Kvasir-SEG/
        |-- TrainDataset/
        |   |-- images/
        |   |   ...
        |   |-- masks/
        |       ...
        |-- ValidationDataset/
        |   |-- images/
        |   |   ...
        |   |-- masks/
        |       ...
        |-- TestDataset/
            |-- images/
            |   ...
            |-- masks/
                ...

2. Training

python main.py --config ./expconfigs/XXX.yaml

3. Testing

python main.py --config ./expconfigs/XXX.yaml --test

Citation

If you feel this work is helpful, please cite our paper

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Comments
  • Question in consistency loss calculation

    Question in consistency loss calculation

    Hello How are you? Thanks for contributing to this project. I found that u used sigmoid rather than softmax in the consistency loss calculation. Of course, the number of segmentation classes is 1 in your project.

    image

    Even when the number of classes is greater than 1, is it right?

    opened by rose-jinyang 0
Owner
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