A deep learning mucosal segmentation algorithm for a mobile detection of oral cancer (mDOC) platform

Mengyuan Xue; Ruchika Mitbander; Jennifer Carns; Richard Schwarz; Nadarajah Vigneswaran; Ann Gillenwater; Rebecca Richards-Kortum

doi:10.1117/12.3034638

21 October 2024 A deep learning mucosal segmentation algorithm for a mobile detection of oral cancer (mDOC) platform

Mengyuan Xue, Ruchika Mitbander, Jennifer Carns, Richard Schwarz, Nadarajah Vigneswaran, Ann Gillenwater, Rebecca Richards-Kortum

Author Affiliations +

Proceedings Volume 13186, SPIE Translational Biophotonics + Additive Manufacturing for Photonics 2024; 1318612 (2024) https://doi.org/10.1117/12.3034638
Event: SPIE Translational Biophotonics + Additive Manufacturing for Photonics, 2024, Houston, Texas, United States

Conference Poster

Abstract

We developed a novel deep-learning based algorithm for a mobile Detection of Oral Cancer (mDOC) platform that captures white light and auto-fluorescence images of the oral cavity. The algorithm first segments images and subsequently identifies suspicious lesions in need of further review by an expert clinician. Preliminary results show a dice score accuracy between ground truth annotated and the network produced segmentation to be higher than 0.9 for the network architectures we tested. This fully automated pipeline enables a data-driven approach with the potential to aid faster diagnosis in the clinic and earlier detection of oral lesions that can ultimately improve patient outcomes.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Mengyuan Xue, Ruchika Mitbander, Jennifer Carns, Richard Schwarz, Nadarajah Vigneswaran, Ann Gillenwater, and Rebecca Richards-Kortum "A deep learning mucosal segmentation algorithm for a mobile detection of oral cancer (mDOC) platform", Proc. SPIE 13186, SPIE Translational Biophotonics + Additive Manufacturing for Photonics 2024, 1318612 (21 October 2024); https://doi.org/10.1117/12.3034638

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