Training-Time Optical Priors for Wireless Capsule Endoscopy Classification: Hemoglobin-Aware Input Fusion with Cross-Vendor Evaluation

Abstract

Gastrointestinal cancers cause approximately 3.4 million deaths annually, and early small-bowel lesions are easily missed at wireless capsule endoscopy (WCE). RGB-trained WCE classifiers conflate hemoglobin contrast with bile staining and illumination falloff, limiting sensitivity to small-vessel vascular findings such as Lymphangiectasia. We introduce a physics-informed framework that injects an analytic, Monte-Carlo-inspired hemoglobin prior into a standard classifier purely at training time -- to our knowledge the first use of an explicit optical light-transport prior in WCE classification. On Kvasir-Capsule (47,238 frames, 43 patients, 11 evaluable classes; patient-disjoint split) we evaluate, across six seeds against an RGB-only EfficientNet-B0 baseline, a five-channel input-fusion variant feeding the prior alongside RGB, a distillation variant that runs on plain three-channel RGB at inference, and a three-stream extension adding a temporal Transformer and an autoencoder-residual stream; we replicate across ResNet-18 and ConvNeXt-Tiny and assess cross-vendor zero-shot transfer on the public Galar cohort. Input fusion lifts cross-seed macro-AUC from 0.760 to 0.783 (5/6 seeds positive); distillation reaches 0.773; the three-stream model reaches 0.804 (+0.044 over baseline, paired DeLong p < 0.0001). Lymphangiectasia AUC rises from 0.238 to 0.337, sign-consistent across all six seeds. A four-variant ablation reveals a parameterization-mechanism boundary: only the spatial-channel form lifts. Cross-vendor zero-shot on Galar retains about 60% of the lift. The distillation variant deploys on plain RGB with a free interpretability heatmap, and we release GalKva-2026, a paired cross-vendor benchmark.

63 pages, 11 figures, 15 tables. Version submitted to Medical Image Analysis. Adds cross-vendor Galar evaluation (GalKva-2026 benchmark), cross-architecture replication (ResNet-18, ConvNeXt-Tiny), and foundation-model baselines. Code, checkpoints, and benchmark: https://github.com/integritynoble/Physics-Informed-PillCam