Deep learning to identify breast disease in an 87-patient clinical study of breast core biopsies to provide rapid biopsy evaluation

Nisha Gandhi; Diana Mojahed; Margherita Firenze; Hua Guo; Hanina Hibshoosh; Richard Ha; Christine Hendon

doi:10.1117/12.2610196

9 March 2022 Deep learning to identify breast disease in an 87-patient clinical study of breast core biopsies to provide rapid biopsy evaluation

Nisha Gandhi, Diana Mojahed, Margherita Firenze, Hua Guo, Hanina Hibshoosh, Richard Ha, Christine Hendon

Author Affiliations +

Proceedings Volume PC11949, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XX; PC119490G (2022) https://doi.org/10.1117/12.2610196
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

Optical coherence tomography (OCT) is being studied to provide rapid biopsy evaluation. Here we developed a deep learning algorithm to rapidly identify disease in OCT images in an 87-patient IRB-approved clinical study. Pathologists labelled each biopsy into two categories: non-interest (no disease) and interest (for further pathological analysis). Our dataset was split by patients into training (n = 70) and validation (n = 17). The Resnet18 architecture used the Adam optimizer, had a learning rate of 0.01, batch size of 8, and ran for 30 epochs. The network achieved 97% training accuracy and 70% validation accuracy.

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Citation Download Citation

Nisha Gandhi, Diana Mojahed, Margherita Firenze, Hua Guo, Hanina Hibshoosh, Richard Ha, and Christine Hendon "Deep learning to identify breast disease in an 87-patient clinical study of breast core biopsies to provide rapid biopsy evaluation", Proc. SPIE PC11949, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XX, PC119490G (9 March 2022); https://doi.org/10.1117/12.2610196

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