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arXiv AI By Johannes Kaiser, Florian Braunmiller, Daniel R\"uckert, Georgios Kaissis

Generalist-Specialist Mixture-of-Experts for Rare Pathology Detection in Multimodal Imaging

Read the original on arXiv AI →

The paper introduces Generalist‑Specialist Mixture‑of‑Experts (GS‑MoE), a two‑branch architecture that combines a cross‑modal generalist model with modality‑specific specialists through domain‑constrained feature fusion. GS‑MoE improves detection of rare pathologies in multimodal medical imaging, achieving significant per‑class F1 gains and outperforming dense and specialist‑only MoE baselines while using about 53% fewer active parameters at inference. The study demonstrates that balancing cross‑modal shared representations with expert routing can enhance performance on low‑prevalence conditions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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