Skip to content

Implementation Code for"Learning Robust Features Alignment for Cross Domain Medical Image Analysis via Dual Consistency Regularizations"

Notifications You must be signed in to change notification settings

gitMrZheng/LRFA-DA

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

10 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Learning Robust Features Alignment for Cross Domain Medical Image Analysis via Dual Consistency Regularizations

We provide skin disease datasets and COVID-19 datasets for domain adaptation from dermatoscopy to smartphones, and from typical pneumonia to COVID-19.

Dataset

Skin diseases classification: ISIC dataset, HAM10000 dataset and PAD-UFES-20 dataset. We collected and integrated the following open-source datasets on skin diseases:

https://www.kaggle.com/datasets/rajivaiml/isic-skin-cancer-dataset

https://www.kaggle.com/datasets/surajghuwalewala/ham1000-segmentation-and-classification

https://data.mendeley.com/datasets/zr7vgbcyr2/1

COVID-19 classification:COVID-19 chest radiograph dataset, COVID-19 X-ray dataset and RSNA pneumonia challenge dataset. Specific links to open source datasets are below:

https://www.kaggle.com/tawsifurrahman/covid19-radiography-database

https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data

About

Implementation Code for"Learning Robust Features Alignment for Cross Domain Medical Image Analysis via Dual Consistency Regularizations"

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages