You are all cordially invited to the first AMLab seminar of 2018 on Tuesday January 16 at 16:00 in C3.163, where Jakub Tomczak will give a talk titled “Deep Multiple Instance Learning with the Attention-based Pooling Operator”. Afterwards there are the usual drinks and snacks!
The computer-aided analysis of medical scans is a longstanding goal in the medical imaging field. Currently, deep learning has became a dominant methodology for supporting pathologists and radiologist. Deep learning algorithms have been successfully applied to digital pathology and radiology, nevertheless, there are still practical issues that prevent these tools to be widely used in practice. The main obstacles are low number of available cases and large size of images (a.k.a. the small n, large p problem in machine learning), and a very limited access to annotation at a pixel level that can lead to severe overfitting and large computational requirements. We propose to handle these issues by introducing a framework that processes a medical image as a collection of small patches using a single, shared neural network. The final diagnosis is provided by combining scores of individual patches using a permutation-invariant operator (combination). In machine learning community such approach is called the multi-instance learning (MIL).
During this presentation we will outline the definition of the MIL and propose a learnable permutation-invariant operator using the attention mechanism. We will provide our preliminary results on a toy problem and real-life histopathology data.
Maximilian Ilse, Jakub Tomczak, Max Welling