EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media

• URL: http://arxiv.org/abs/2505.21532v1
• Date: Fri, 23 May 2025 16:38:13 GMT
• Title: EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media
• Authors: Ismail Erbas, Ferhat Demirkiran, Karthik Swaminathan, Naigang Wang, Navid Ibtehaj Nizam, Stefan T. Radev, Kaoutar El Maghraoui, Xavier Intes, Vikas Pandey,
• Abstract summary: Fluorescence LiDAR (FLiDAR) is a technology employed for distance and depth estimation across medical, automotive, and other fields.<n>The complex nature of the acquired FLiDAR signal makes isolating photon time-of-flight and intrinsic fluorescence lifetime exceptionally difficult.<n>We present a Physics-Guided Mixture-of-Experts framework tailored for specialized modeling of diverse temporal components.
• Score: 3.9833452895674655
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fluorescence LiDAR (FLiDAR), a Light Detection and Ranging (LiDAR) technology employed for distance and depth estimation across medical, automotive, and other fields, encounters significant computational challenges in scattering media. The complex nature of the acquired FLiDAR signal, particularly in such environments, makes isolating photon time-of-flight (related to target depth) and intrinsic fluorescence lifetime exceptionally difficult, thus limiting the effectiveness of current analytical and computational methodologies. To overcome this limitation, we present a Physics-Guided Mixture-of-Experts (MoE) framework tailored for specialized modeling of diverse temporal components. In contrast to the conventional MoE approaches our expert models are informed by underlying physics, such as the radiative transport equation governing photon propagation in scattering media. Central to our approach is EvidenceMoE, which integrates Evidence-Based Dirichlet Critics (EDCs). These critic models assess the reliability of each expert's output by providing per-expert quality scores and corrective feedback. A Decider Network then leverages this information to fuse expert predictions into a robust final estimate adaptively. We validate our method using realistically simulated Fluorescence LiDAR (FLiDAR) data for non-invasive cancer cell depth detection generated from photon transport models in tissue. Our framework demonstrates strong performance, achieving a normalized root mean squared error (NRMSE) of 0.030 for depth estimation and 0.074 for fluorescence lifetime.

Related papers

• Shape from Polarization of Thermal Emission and Reflection [2.7317088388886384]
  We leverage the Shape from Polarization (SfP) technique in the Long-Wave Infrared (LWIR) spectrum, where most materials are opaque and emissive.<n>We formulated a polarization model that explicitly accounts for the combined effects of emission and reflection.<n>We implemented a prototype system and created ThermoPol, the first real-world benchmark dataset for LWIR SfP.
  arXiv  Detail & Related papers  (2025-06-23T00:33:17Z)
• Provable Maximum Entropy Manifold Exploration via Diffusion Models [58.89696361871563]
  Exploration is critical for solving real-world decision-making problems such as scientific discovery.<n>We introduce a novel framework that casts exploration as entropy over approximate data manifold implicitly defined by a pre-trained diffusion model.<n>We develop an algorithm based on mirror descent that solves the exploration problem as sequential fine-tuning of a pre-trained diffusion model.
  arXiv  Detail & Related papers  (2025-06-18T11:59:15Z)
• Comparative analysis of Realistic EMF Exposure Estimation from Low Density Sensor Network by Finite & Infinite Neural Networks [0.6144680854063939]
  In this work, we present both finite and infinite-width convolutional network-based methods to estimate and assess EMF exposure levels.<n>A comparative analysis has been conducted to analyze the performance of the methods' execution time and estimation accuracy.
  arXiv  Detail & Related papers  (2025-04-07T12:31:53Z)
• Unified, Verifiable Neural Simulators for Electromagnetic Wave Inverse Problems [1.6795461001108096]
  We show a single, unified model capable of addressing scattering simulations with thousands of DoFs, of any wavelength, any illumination wavefront, and freeform materials. Our findings demonstrate a path to universal, verifiably accurate neural surrogates for existing scattering simulators.
  arXiv  Detail & Related papers  (2024-03-31T03:23:29Z)
• Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution [23.34647832483913]
  We introduce a framework known as Adaptive Multi-modal Fusion of textbfSpatially Variant Kernel Refinement with Diffusion Model for Blind Image textbfSuper-textbfResolution. We also introduce the Adaptive Multi-Modal Fusion (AMF) module to align the information from three modalities: low-resolution images, depth maps, and blur kernels.
  arXiv  Detail & Related papers  (2024-03-09T06:01:25Z)
• Deep Learning reconstruction with uncertainty estimation for $\gamma$ photon interaction in fast scintillator detectors [1.0149560203037322]
  This article presents a physics-informed deep learning method for the quantitative estimation of the spatial coordinates of gamma interactions within a monolithic scintillator. A Density Neural Network approach is designed to estimate the 2-dimensional gamma photon interaction coordinates in a fast lead tungstate detector.
  arXiv  Detail & Related papers  (2023-10-10T12:31:29Z)
• The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation [42.48819460873482]
  Denoising diffusion probabilistic models have transformed image generation with their impressive fidelity and diversity. We show that they also excel in estimating optical flow and monocular depth, surprisingly, without task-specific architectures and loss functions.
  arXiv  Detail & Related papers  (2023-06-02T21:26:20Z)
• Low-Light Image Enhancement with Wavelet-based Diffusion Models [50.632343822790006]
  Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. We propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL.
  arXiv  Detail & Related papers  (2023-06-01T03:08:28Z)
• End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics [61.44793171735013]
  We introduce a novel unified architecture, termed latent variation models, which combines the latent learning of cutting-edge generative art approaches with an end-to-end variational framework. Our unified approach achieves a distribution-free distance to the truth of over 20 times less than non-latent state-of-the-art baseline.
  arXiv  Detail & Related papers  (2023-05-17T17:43:10Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)
• Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso [102.84915019938413]
  Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques. The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge. We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
  arXiv  Detail & Related papers  (2020-09-29T21:17:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.