Bayesian deep learning for reliable oral cancer image classification

Bofan Song, Sumsum Sunny, Shaobai Li, Keerthi Gurushanth, Pramila Mendonca, Nirza Mukhia, Sanjana Patrick, Shubha Gurudath, Subhashini Raghavan, Imchen Tsusennaro, Shirley T. Leivon, Trupti Kolur, Vivek Shetty, Vidya R. Bushan, Rohan Ramesh, Tyler Peterson, Vijay Pillai, Petra Wilder-Smith, Alben Sigamani, Amritha SureshMoni Abraham Kuriakose, Praveen Birur, Rongguang Liang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In medical imaging, deep learning-based solutions have achieved state-of-the-art performance. However, reliability restricts the integration of deep learning into practical medical workflows since conventional deep learning frameworks cannot quantitatively assess model uncertainty. In this work, we propose to address this shortcoming by utilizing a Bayesian deep network capable of estimating uncertainty to assess oral cancer image classification reliability. We evaluate the model using a large intraoral cheek mucosa image dataset captured using our customized device from high-risk population to show that meaningful uncertainty information can be produced. In addition, our experiments show improved accuracy by uncertainty-informed referral. The accuracy of retained data reaches roughly 90% when referring either 10% of all cases or referring cases whose uncertainty value is greater than 0.3. The performance can be further improved by referring more patients. The experiments show the model is capable of identifying difficult cases needing further inspection.

Original languageEnglish (US)
Pages (from-to)6422-6430
Number of pages9
JournalBiomedical Optics Express
Volume12
Issue number10
DOIs
StatePublished - Oct 1 2021

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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