Generalist-Specialist Mixture-of-Experts for Rare Pathology Detection in Multimodal Imaging
Paper recorded by Signals 4 on 2026-09-16 in cs.CV. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17
Category: cs.CV · 计算机视觉 · first seen 2026-09-17
Abstract
AI models for multimodal medical imaging must balance modality-specific specialization with cross-modal shared representations, a trade-off that pure Mixture-of-Experts (MoE) architectures currently fail to satisfy. Expert-based routing improves in-domain learning but may sacrifice cross-modal signals, which appear particularly important for rare (low-prevalence) pathologies in our experiments. To
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Cite this page: Generalist-Specialist Mixture-of-Experts for Rare Pathology Detection in Multimodal Imaging: the #28 most recent of 237 cs.CV papers we have recorded (as of 2026-09-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/generalist-specialist-mixture-of-experts-for-rare-pathology-detection-in-multimo.html
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