oopil / atten_deep_mil Goto Github PK

This is a re-implementation of ICML 2018 paper "Attention-based Deep Multiple Instance Learning" (https://arxiv.org/pdf/1802.04712.pdf). The official Pytorch implementation can be found here.

I believe it is a very interesting work and so I built it with Keras using Tensorflow backend. I wrote attention layers described in the paper and did experiments in colon images with 10-fold cross validation. I got the very close average accuracy described in the paper and visualization results can be seen as below. Parts of codes are from https://github.com/yanyongluan/MINNs.

When train the model, we only use the image-level label (0 or 1 to see if it is a cancer image). The attention layer can provide an interpretation of the decision by presenting only a small subset of positive patches.

• Colon cancer dataset [Data]
• Processed patches [Google Drive]

I put my processed data here and you can also set up according to the paper. If you have any problem, please feel free to contact me.