Sample code for the prostate cancer localization

Introduction

We propose a co-trained prostate cancer localization framework for prostate cancer localization with a weakly supervised multi-modal network. It highlights the most informative regions relevant to the predicted prostate cancer class. You could get attention-based model instantly by tweaking your own CNN a little bit more. The paper is published at MIA'17.

The framework of the prostate cancer localization is as below:

Some predicted prostate activation maps for cancer region are:

Citing co-trained multi-modal in your publications if it helps your research:

@article{yang2017co,
  title={Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI},
  author={Yang, Xin and Liu, Chaoyue and Wang, Zhiwei and Yang, Jun and Le Min, Hung and Wang, Liang and Cheng, Kwang-Ting Tim},
  journal={Medical image analysis},
  volume={42},
  pages={212--227},
  year={2017},
  publisher={Elsevier}
}

Usage Instructions:

• Install caffe, compile the matcaffe (matlab wrapper for caffe), and make sure you could run the prediction example code classification.m.
• Clone the code from Github:
• Run the demo code to generate the heatmap in matlab terminal
• create lmdb

cd models
ipython notebook main.ipynb

• train

python models/solveCAM_cov_loss_min.py

• eval in matlab terminal

evaluationdualloss.m

• get feature in matlab terminal

featureextractloss.m

Pre-trained Models:

cd model