# Learning to Segment Breast Biopsy Whole Slide Images Source: [Learning to Segment Breast Biopsy Whole Slide Images]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:47:17.894Z Authors: Sachin Mehta, Ezgi Mercan, Jamen Bartlett, Donald Weaver, Joann Elmore, Linda Shapiro Published: 2017-09-08T06:20:23.000Z ## Abstract We trained and applied an encoder-decoder model to semantically segment breast biopsy images into biologically meaningful tissue labels. Since conventional encoder-decoder networks cannot be applied directly on large biopsy images and the different sized structures in biopsies present novel challenges, we propose four modifications: (1) an input-aware encoding block to compensate for information loss, (2) a new dense connection pattern between encoder and decoder, (3) dense and sparse decoders to combine multi-level features, (4) a multi-resolution network that fuses the results of encoder-decoders run on different resolutions. Our model outperforms a feature-based approach and conventional encoder-decoders from the literature. We use semantic segmentations produced with our model in an automated diagnosis task and obtain higher accuracies than a baseline approach that employs an SVM for feature-based segmentation, both using the same segmentation-based diagnostic features. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on Breast biopsy (val)](): Semantic segmentation; Breast biopsy (val); mIoU: 44.2. ## Original sources and implementation [Original paper]() [PDF]()