Incorporating longitudinal changes of mammograms for breast cancer diagnosis

Zhengbo Zhou; Dooman Arefan; Margarita L. Zuley; Jules H. Sumkin; Shandong Wu

doi:10.1117/12.3008804

3 April 2024 Incorporating longitudinal changes of mammograms for breast cancer diagnosis

Zhengbo Zhou, Dooman Arefan, Margarita L. Zuley, Jules H. Sumkin, Shandong Wu

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292724 (2024) https://doi.org/10.1117/12.3008804
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

In recent years, deep learning has showcased substantial promise in breast cancer diagnosis via mammograms. However, the integration of longitudinal changes between consecutive mammograms, which clinicians frequently consider for diagnosis, remains under-explored. In this study, we introduce an novel method that leverages crossattention mechanisms to capture longitudinal information between consecutive mammograms taken at various time intervals. Our method’s efficacy was assessed using a case-control internal dataset consisting of 590 cases. Preliminary results underscore its superiority over models relying solely on a single ”current” mammogram exam and those that merely combine features extracted from ”current” and ”prior” mammograms. By harnessing the power of longitudinal data, our model achieved enhanced diagnostic performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhengbo Zhou, Dooman Arefan, Margarita L. Zuley, Jules H. Sumkin, and Shandong Wu "Incorporating longitudinal changes of mammograms for breast cancer diagnosis", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292724 (3 April 2024); https://doi.org/10.1117/12.3008804

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