  Breast cancer has the highest mortality among cancers in women.
Computer-aided pathology to analyze microscopic histopathology images for
diagnosis with an increasing number of breast cancer patients can bring the
cost and delays of diagnosis down. Deep learning in histopathology has
attracted attention over the last decade of achieving state-of-the-art
performance in classification and localization tasks. The convolutional neural
network, a deep learning framework, provides remarkable results in tissue
images analysis, but lacks in providing interpretation and reasoning behind the
decisions. We aim to provide a better interpretation of classification results
by providing localization on microscopic histopathology images. We frame the
image classification problem as weakly supervised multiple instance learning
problem where an image is collection of patches i.e. instances. Attention-based
multiple instance learning (A-MIL) learns attention on the patches from the
image to localize the malignant and normal regions in an image and use them to
classify the image. We present classification and localization results on two
publicly available BreakHIS and BACH dataset. The classification and
visualization results are compared with other recent techniques. The proposed
method achieves better localization results without compromising classification
accuracy.
