This is the code for benchmark comparing self-supervised and semi-supervised deep classifiers for medical images

Here we provide the Supplementary Materials[Appendix.pdf/] of our benchmark. The Supplement includes following sections to describe the experiments and analysis in more details.

• Code and Data Splits for Reproducibility
• Dataset Details
• Additional Results
• Algorithms Details
• Additional Analysis

• TissueMNIST and PathMNIST: please visit https://zenodo.org/record/6496656
• AIROGS: please visit: https://zenodo.org/record/5793241
• TMED2: please visit https://TMED.cs.tufts.edu and follow the instruction. We use the split1 in the released data.

Follow the instructions here: https://conda.io/projects/conda/en/latest/user-guide/install/index.html

packages needed are specified in environment.yml (TODO)

export ROOT_PATH="paths to this repo" (e.g., '/ab/cd/SSL-vs-SSL-benchmark', then do export ROOT_PATH = '/ab/cd/SSL-vs-SSL-benchmark')

For example if you want to run FixMatch on TissueMNIST to reproduce Figure 1(a) and Figure A.2(a), go to runs/TissueMNIST/FixMatch/ bash launch_experiment.sh run_here

Note that you will need to edit the paths to dataset in the launch_experiment.sh file.

While the focus of our paper is reproducibility, ultimately exact comparison to the results in our paper will be conflated by subtle differences such as the version of Pytorch etc (see https://pytorch.org/docs/stable/notes/randomness.html for more detail).

This repository builds upon the public repo pytorch-consistency-regularization[https://github.com/perrying/pytorch-consistency-regularization]. Thanks for sharing the great code bases!

TODO