This is the official code repository for the CiBM 2024 paper "Coarse-to-fine Visual Representation Learning for Medical Images via Class Activation Maps".
python -m virtualenv -p 3.9 env
source env/bin/activate
pip install -r requirements.txt
python setup.py install
Dataset | Task | Link |
---|---|---|
OIA-ODIR | Fundus pretraining | original preprocessed |
ChestX-ray14 | X-ray pretraining Thorax disease classification |
original preprocessed |
IDRiD | DR-DME classification Lesions segmentation |
source |
REFUGE | Glaucoma classification Disc/cup segmentation |
source |
Vessel-seg | Vessel segmentation | DRIVE STARE CHASE_DB1 |
SIIM-ACR | Pneumothorax segmentation | source |
- preprocessing and pretraining:
script/upstream
- transfer learning:
script/downstream
- shell scripts with commands for reproducing the experimental results:
example
, e.g., executeexample/upstream/chestx-ray14/0_preprocess.sh
to run the preprocessing step
The implementation of contrastive learning loss was adapted from the SupContrast repository.
@article{YAP2024108203,
title = {Coarse-to-fine visual representation learning for medical images via class activation maps},
journal = {Computers in Biology and Medicine},
volume = {171},
pages = {108203},
year = {2024},
issn = {0010-4825},
doi = {https://doi.org/10.1016/j.compbiomed.2024.108203},
url = {https://www.sciencedirect.com/science/article/pii/S0010482524002877},
author = {Boon Peng Yap and Beng Koon Ng}
}