TY - GEN
T1 - Reliable oral cancer classification framework with Bayesian deep learning
AU - Song, Bofan
AU - Sunny, Sumsum
AU - Li, Shaobai
AU - Keerthi, G.
AU - Patrick, Sanjana
AU - Mukhia, Nirza
AU - Gurudath, Shubha
AU - Raghavan, Subhashini
AU - Mendonca, Pramila
AU - Tsusennaro,
AU - Leivon, Shirley T.
AU - Kolur, Trupti
AU - Shetty, Vivek
AU - Vidya Bushan, R.
AU - Ramesh, Rohan
AU - Pillai, Vijay
AU - Sigamani, Alben
AU - Suresh, Amritha
AU - Kuriakose, moni Abraham
AU - Birur, Praveen
AU - Liang, Rongguang
N1 - Publisher Copyright:
© OSA 2020 © 2020 The Author(s)
PY - 2020/9/14
Y1 - 2020/9/14
N2 - Standard deep learning algorithms for clinical image classification are unable to understand their confidence in a decision. We developed a Bayesian deep network could estimate uncertainty to assess the reliability of oral cancer image classification.
AB - Standard deep learning algorithms for clinical image classification are unable to understand their confidence in a decision. We developed a Bayesian deep network could estimate uncertainty to assess the reliability of oral cancer image classification.
UR - https://www.scopus.com/pages/publications/85105992262
UR - https://www.scopus.com/pages/publications/85105992262#tab=citedBy
U2 - 10.1364/FIO.2020.JM6B.18
DO - 10.1364/FIO.2020.JM6B.18
M3 - Conference contribution
AN - SCOPUS:85105992262
T3 - Optics InfoBase Conference Papers
BT - Frontiers in Optics - Proceedings Frontiers in Optics / Laser Science, Part of Frontiers in Optics + Laser Science APS/DLS, FiO 2020
PB - Optica Publishing Group (formerly OSA)
T2 - 2020 Frontiers in Optics Conference, FiO 2020
Y2 - 14 September 2020 through 17 September 2020
ER -