Multi-Feature-Semi-Supervised-Learning_COVID-19 (Pytorch)

Introduction

This is the code to repoduce the study of Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-ray Images. Please cite our study if you are using this dataset and referring to our method.

Network Structure

Result

• Test-1

Method	Labeled Sample (%)	Precision	Recall	F1-Scores	Top-1(%)
MF-TS	30	0.94	0.94	0.94	93.61

• Test-2

Method	Labeled Sample (%)	Precision	Recall	F1-Scores	Top-1(%)
MF-TS	30	0.93	0.94	0.93	92.47

Usage

• Dataset and Trained model weights:

  • Download them from Kaggle. CXR folder are all origianl CXR images and Enh folder are all corresponding enhanced images. All weights are in the weight folder

• Preparation:

  • Create a folder to save all downloaded files from this repo and files from Kaggle in one folder. Please modify the coloumn of both test_ds.txt and additional_test_ds.txt to the directory where you create the folder

• Test:

  • CXR-TS: python Test.py --action=retest --dataset=test_ds/additional_test_ds --per_teacher=0.1/0.2/0.3 (test_ds=Test-1; additiona_test_ds=Test-2; 0.1=10% labeled samples etc.)
  • Enh-TS: python Test.py --action=retest --dataset=test_ds/additional_test_ds --type=Enh --per_teacher=0.1/0.2/0.3 (type = image type; default = CXR)
  • MF-T: python Test.py --action=retest_based_both --dataset=test_ds/additional_test_ds --per_teacher=0.1/0.2/0.3
  • MF-TS: python Test.py --action=latefusion_retest_2models --dataset=test_ds/additional_test_ds --per_teacher=0.1/0.2/0.3