yNet: A Breast Ultrasound Image Classification Algorithm Based on Metric Learning.
- python >= 3.9
- pytorch >= 1.7
- pytorch-lightning == 1.3.1
- opencv-python
- rich
- omegaconf
See scripts in src/data/script
for detail.
Here is an example of compiling BUSI using vscode task:
{
"label": "make dataset: BUSI",
"type": "shell",
"command": [
"python src/data/script/makelabel.py BUSI --sets benign:0 malignant:1 --title Ym mask;",
"python src/data/script/resize.py BUSI --sets benign malignant;",
"python src/data/script/makecaches.py BUSI --sets benign malignant --title Ym mask --stat Ym;",
],
"problemMatcher": []
}
We recommend to train yNet on a GPU with 18000M free memory. Our implement uses RTX3090.
Multi-processing is not implemented. So DDP isn't available. We warn of using DP directly, since triplet loss and BN are used. Fork is welcomed.
python3.9 src/train_toynetv1.py # paths.version=QAQ
You may need to edit .env
on Linux.