Multiple Instance Learning Via Deep Hierarchical Exploration for Histology Image Classification

We present a fast hierarchical method to detect a presence of cancerous tissue in histological images. The image is not examined in detail everywhere but only inside several small regions of interest, called glimpses. The final classification is done by aggregating classification scores from a CNN on leaf glimpses at the highest resolution. Unlike in existing attention-based methods, the glimpses form a tree structure, low resolution glimpses determining the location of several higher resolution glimpses using weighted sampling and a CNN approximation of the expected scores. We show that it is possible to perform the classification with just a small number of glimpses, leading to an important speedup with only a small performance deterioration. Learning is possible using image labels only, as in the multiple instance learning (MIL) setting.