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Proceedings Paper

Performance comparison of different loss functions for digital breast tomosynthesis classification using 3D deep learning model
Author(s): Emine Doganay; Yahong Luo; Long Gao; Puchen Li; Wendie Berg; Shandong Wu
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Paper Abstract

Artificial intelligence (AI) algorithms, especially deep learning methods have proven to be successful in many medical imaging applications. Computerized breast cancer image analysis can improve diagnosis accuracy. Digital Breast Tomosynthesis (DBT) imaging is a new modality and more advantageous compared to classical digital mammography (DM). Therefore, development of new deep learning algorithms compatible with DBT modality are potent to improve DBT imaging reading time efficiency and increase accuracy for breast cancer diagnosis when used as additional tool for radiologists. In this work, we aimed to build a 3D deep learning model to distinguish malignancy and benign breasts using DBT images. We also investigated effects of different loss functions in our deep learning models. We implemented and evaluated our method on a large data set of 546 patients (205 malignancy and 341 benign). Our results showed that different loss functions lead to an influence on the models performance in our classification tasks, and specific loss function may be selected or customized to adjust a specific performance metric for concrete applications.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141A (16 March 2020); doi: 10.1117/12.2551373
Show Author Affiliations
Emine Doganay, Univ. of Pittsburgh (United States)
Yahong Luo, Liaoning Cancer Hospital and Institute (China)
Long Gao, National Univ. of Defense Technology (China)
Puchen Li, Liaoning Cancer Hospital and Institute (China)
Wendie Berg, Univ. of Pittsburgh (United States)
Shandong Wu, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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