AI-assisted simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy for the discovery of human breast cancer-related optical biomarkers

Jindou Shi; Haohua Tu; Jaena Park; Stephen A. Boppart

doi:10.1117/12.2608857

7 March 2022 AI-assisted simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy for the discovery of human breast cancer-related optical biomarkers

Jindou Shi, Haohua Tu, Jaena Park, Stephen A. Boppart

Author Affiliations +

Proceedings Volume PC11952, Multimodal Biomedical Imaging XVII; PC1195205 (2022) https://doi.org/10.1117/12.2608857
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

Simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy produces multimodal high-resolution images with rich functional and structural information from living tissue. Here we present a deep learning framework for human breast cancer-related optical biomarker discovery based on SLAM. This framework consists of three stages: self-supervised consistency training for image representation learning at multiple scales; cancer region identification by Multiple Instance Learning; optical biomarker discovery based on channel-wise attribution maps. This study demonstrates the capability of AI-assisted SLAM microscopy in capturing rich information from living tissue and extracting relationships between optical features with human breast cancer, which can be extended to various types of diseases and treatment conditions.

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Jindou Shi, Haohua Tu, Jaena Park, and Stephen A. Boppart "AI-assisted simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy for the discovery of human breast cancer-related optical biomarkers", Proc. SPIE PC11952, Multimodal Biomedical Imaging XVII, PC1195205 (7 March 2022); https://doi.org/10.1117/12.2608857

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KEYWORDS

Microscopy

Breast

Tissue optics

Tumors

Breast cancer

Optical microscopy

Profiling

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