  In this paper, we introduce a conceptually simple network for generating
discriminative tissue-level segmentation masks for the purpose of breast cancer
diagnosis. Our method efficiently segments different types of tissues in breast
biopsy images while simultaneously predicting a discriminative map for
identifying important areas in an image. Our network, Y-Net, extends and
generalizes U-Net by adding a parallel branch for discriminative map generation
and by supporting convolutional block modularity, which allows the user to
adjust network efficiency without altering the network topology. Y-Net delivers
state-of-the-art segmentation accuracy while learning 6.6x fewer parameters
than its closest competitors. The addition of descriptive power from Y-Net's
discriminative segmentation masks improve diagnostic classification accuracy by
7% over state-of-the-art methods for diagnostic classification. Source code is
available at: https://sacmehta.github.io/YNet.
