Breast cancer classification from digital breast tomosynthesis using 3D multi-subvolume approach

Emine Doganay; Puchen Li; Yahong Luo; Ruimei Chai; Yuan Guo; Shandong Wu

doi:10.1117/12.2551376

2 March 2020 Breast cancer classification from digital breast tomosynthesis using 3D multi-subvolume approach

Emine Doganay, Puchen Li, Yahong Luo, Ruimei Chai, Yuan Guo, Shandong Wu

Proceedings Volume 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications; 113180D (2020) https://doi.org/10.1117/12.2551376
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Digital mammography (DM) was the most common image guided diagnostic tool in breast cancer detection up till recent years. However, digital breast tomosynthesis (DBT) imaging, which presents more accurate results than DM, is going to replace DM in clinical practice. As in many medical image processing applications, Artificial Intelligence (AI) has been shown promising in reducing radiologists reading time with enhanced cancer diagnostic accuracy. In this paper, we implemented a 3D network using deep learning algorithms to detect breast cancer malignancy using DBT craniocaudal (CC) view images. We created a multi-sub-volume approach, in which the most representative slice (MRS) for malignancy scans is manually selected/defined by expert radiologists. We specifically compared the effects on different selections of the MRS by two radiologists and the resulting model performance variations. The results indicate that our scheme is relatively robust for all three experiments.

Conference Presentation

Citation Download Citation

Emine Doganay, Puchen Li, Yahong Luo, Ruimei Chai, Yuan Guo, and Shandong Wu "Breast cancer classification from digital breast tomosynthesis using 3D multi-subvolume approach", Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 113180D (2 March 2020); https://doi.org/10.1117/12.2551376

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