Classification of breast lesions with deep learning combining diffuse optical tomography frequency-domain data and coregistered ultrasound images

Menghao Zhang; Shuying Li; Minghao Xue; Quing Zhu

doi:10.1117/12.2650179

7 March 2023 Classification of breast lesions with deep learning combining diffuse optical tomography frequency-domain data and coregistered ultrasound images

Menghao Zhang, Shuying Li, Minghao Xue, Quing Zhu

Author Affiliations +

Proceedings Volume PC12376, Optical Tomography and Spectroscopy of Tissue XV; PC1237609 (2023) https://doi.org/10.1117/12.2650179
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential for breast cancer diagnosis. Previous diagnostic strategies all require image reconstruction, which hindered real-time diagnosis. In this study, we propose a deep learning approach to combine DOT frequency-domain measurement data and co-registered US images to classify breast lesions. The combined deep learning model achieved an AUC of 0.886 in distinguishing between benign and malignant breast lesions in patient data without reconstructing images.

Conference Presentation

Citation Download Citation

Menghao Zhang, Shuying Li, Minghao Xue, and Quing Zhu "Classification of breast lesions with deep learning combining diffuse optical tomography frequency-domain data and coregistered ultrasound images", Proc. SPIE PC12376, Optical Tomography and Spectroscopy of Tissue XV, PC1237609 (7 March 2023); https://doi.org/10.1117/12.2650179

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