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

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.