Advancing Fluorescence Detection and Ranging in Scattering Media with Mixture-of-Experts and Evidential Critics

Ismail Erbas, Ferhat Demirkiran, Karthik Swaminathan, Naigang Wang, Navid Ibtehaj Nizam, Nanxue Yuan, Saif Ragab, Luis Chavez, Kaoutar El Maghraoui, Xavier Intes, Vikas Pandey

Submission Track: Short papers presenting ongoing research or work submitted to other venues (up to 5 pages, excluding references)

Keywords: Mixture of Experts, Physics-guided, Evidential Deep Learning, Flourescence LiDAR, Scattering Media, Time-Resolved LiDAR

Abstract: Attention-based models dominate sequence transduction, yet in medical time-series datasets they often misallocate focus to irrelevant regions while missing critical context. We present EvidenceMoE, a Mixture-of-Experts architecture that assigns experts based on prior physics knowledge and refines their outputs through an Evidential Dirichlet feedback mechanism providing per-expert reliability scores. In our work on fluorescence lifetime-guided cancer surgery, we assigned expert models to relevant time-series segments encoding tumor depth and microenvironment based tumor delineation knowledge from physics (i.e., the radiative transport equation for photon propagation in tissue), rather than learned only from data. Unlike other prior models that address either depth (e.g., Fluorescence LiDAR) or fluorescence decay (fluorescence lifetime or FLI for drug–target binding), EvidenceMoE jointly captures both within a unified framework, achieving errors as low as 0.030 NRMSE for depth and 0.074 NRMSE for FLI on simulated and experimental datasets, closely matching ground-truth measurements.

Submission Number: 29