Real-time colorectal cancer diagnosis using PR-OCT with deep learning

Yifeng Zeng; Shiqi Xu; William C. Chapman; Shuying Li; Zahra Alipour; Heba Abdelal; Deyali Chatterjee; Matthew Mutch; Quing Zhu; Quing Zhu

doi:10.1364/OCT.2020.OW2E.5

Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN)
OSA Technical Digest (Optica Publishing Group, 2020),
paper OW2E.5
•https://doi.org/10.1364/OCT.2020.OW2E.5

Real-time colorectal cancer diagnosis using PR-OCT with deep learning

Yifeng Zeng, Shiqi Xu, William C. Chapman, Shuying Li, Zahra Alipour, Heba Abdelal, Deyali Chatterjee, Matthew Mutch, and Quing Zhu

Optical Coherence Tomography 2020

Washington, DC United States
20–23 April 2020
ISBN: 978-1-943580-74-3

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Image Processing, Machine Learning, and Deep Learning Algorithms (OW2E)

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Y. Zeng, S. Xu, W. C. Chapman, S. Li, Z. Alipour, H. Abdelal, D. Chatterjee, M. Mutch, and Q. Zhu, "Real-time colorectal cancer diagnosis using PR-OCT with deep learning," in Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN), OSA Technical Digest (Optica Publishing Group, 2020), paper OW2E.5.

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Abstract

A pattern-recognition optical coherence tomography was designed to automate diagnosis of human colorectal tissue in real-time. An area under the ROC of 0.998 is achieved by our initial experience with 18 ex vivo human specimens.

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