DFLNet: Disentangled Feature Learning Network for Breast Cancer Ultrasound Image Segmentation

⭐ This code has been completely released ⭐

Overview

Requirements

Python 3.6
Pytorch 1.7.0

Datasets Preparation

• The download link for the datasets is here. Put the datasets as follows:

DFLNet
├── BUSI-WHU
│   ├── train
│   │   ├── img
│   │   │   ├── 00001.bmp
│   │   │   ├── 00002.bmp
│   │   │   ├── .....
│   │   ├── gt
│   │   │   ├── 00001.bmp
│   │   │   ├── 00002.bmp
│   │   │   ├── .....
│   ├── valid
│   │   ├── img
│   │   │   ├── 00009.bmp
│   │   │   ├── 00015.bmp
│   │   │   ├── .....
│   │   ├── gt
│   │   │   ├── 00009.bmp
│   │   │   ├── 00015.bmp
│   │   │   ├── .....
│   ├── test
│   │   ├── img
│   │   │   ├── 00007.bmp
│   │   │   ├── 00008.bmp
│   │   │   ├── .....
│   │   ├── gt
│   │   │   ├── 00007.bmp
│   │   │   ├── 00008.bmp
│   │   │   ├── .....

Train

Modify the paths in lines 22 to 30 of the train.py, then simply run:

python train.py

• The download link for the pretrain_pth is here.

Test

Modify the paths in lines 14 to 15 of the eval.py, then simply run:

python eval.py

Visualization

Modify the paths in lines 13 to 17 of the visualization.py, then simply run:

python visualization.py

• Note that batch-size must be 1 when using visualization.py
• Besides, you can adjust the parameter of full_to_color to change the color

Visual Results