Introduction

yNet: A Breast Ultrasound Image Classification Algorithm Based on Metric Learning.

Requirements

• python >= 3.9
• pytorch >= 1.7
• pytorch-lightning == 1.3.1
• opencv-python
• rich
• omegaconf

Usage

compile datasets

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": []
}

training

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.