Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images
This work addresses breast cancer diagnosis for medical imaging, offering an incremental improvement by extending U-Net with a parallel branch for enhanced segmentation and classification.
The paper tackles breast cancer diagnosis by introducing Y-Net, a network that segments tissues in biopsy images and generates discriminative maps, achieving state-of-the-art segmentation with 6.6x fewer parameters and improving diagnostic classification accuracy by 7% over prior methods.
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.