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Analysis of Regions of Interest and Distractor Regions in Breast Biopsy Images. | LitMetric

Analysis of Regions of Interest and Distractor Regions in Breast Biopsy Images.

IEEE EMBS Int Conf Biomed Health Inform

Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle.

Published: July 2021

AI Article Synopsis

  • - The paper examines how pathologists can make misdiagnoses in complex breast biopsy cases by analyzing a data set of 240 whole slide images (WSIs) and establishing consensus diagnoses among experienced experts.
  • - A group of 87 other pathologists attempted to diagnose test sets of 60 slides each, marking their regions of interest (ROIs); discrepancies between their ROIs and the consensus ones indicated incorrect diagnoses.
  • - The research utilized a deep learning classifier, HATNet, to assess visual similarities and differences in the ROIs, revealing challenges in feature classification and highlighting the study's potential to improve pathologist training in breast biopsy diagnostics.

Article Abstract

This paper studies pathologists can misdiagnose diagnostically challenging breast biopsy cases, using a data set of 240 whole slide images (WSIs). Three experienced pathologists agreed on a consensus reference ground-truth diagnosis for each slide and also a consensus region of interest (ROI) from which the diagnosis could best be made. A study group of 87 other pathologists then diagnosed test sets (60 slides each) and marked their own regions of interest. Diagnoses and ROIs were categorized such that if on a given slide, their ROI differed from the consensus ROI their diagnosis was incorrect, that ROI was called a . We used the HATNet transformer-based deep learning classifier to evaluate the visual similarities and differences between the true (consensus) ROIs and the distractors. Results showed high accuracy for both the similarity and difference networks, showcasing the challenging nature of feature classification with breast biopsy images. This study is important in the potential use of its results for teaching pathologists how to diagnose breast biopsy slides.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9801511PMC
http://dx.doi.org/10.1109/bhi50953.2021.9508513DOI Listing

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