Concept-Guided Multimodal AI for Interpretable Pathology
A new AI architecture, ConceptM^3oE, aims to make computational pathology more interpretable by embedding concept formation within mixture-of-experts pathways. The model processes whole-slide images, pathology reports, and molecular measurements, decomposing evidence into modality-specific, redundant, and synergistic experts. These are projected into structured concept bottlenecks that map to morphology and biomarker hierarchies. A residual pathway prevents information loss typical of interpretable bottlenecks. The approach addresses the challenge of distinguishing complex tumor subtypes where morphology alone is insufficient. The paper is published on arXiv.
Key facts
- ConceptM^3oE stands for Concept Multimodal Mixture of Experts.
- The architecture decomposes evidence into modality-specific, redundant, and synergistic experts.
- It uses structured concept bottlenecks mapping to morphology and biomarker concepts.
- A residual pathway prevents information loss.
- The model processes whole-slide images, pathology reports, and molecular measurements.
- It targets complex tumor subtypes where morphology alone is challenging.
- The paper is available on arXiv with ID 2605.24399.
- Healthcare models are transitioning from unimodal prediction to multimodal reasoning.
Entities
Institutions
- arXiv