Work

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

Year: 2021

Type: conference-paper

Abstract: This paper studies why 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 and their diagnosis was incorrect, that ROI was called a distractor. 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.

Source: IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...)

DOI: 10.1109/bhi50953.2021.9508513

Authors: Ximing Lu, Sachin Mehta, Tad T. Brunye, Donald L. Weaver, Joann G. Elmore, Linda G. Shapiro

Institutions: University of Washington, Tufts University, University of Vermont, University of California, Los Angeles

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Cancer Institute