Pathway-Structured Privileged Distillation for Deployable Computational Pathology
Quick Take
The Mixture of Pathway Experts (MoPE) framework enhances histology-only inference by integrating RNA-derived pathways, achieving improved performance on public benchmarks and independent breast cancer cohorts. This method allows for effective cancer risk modeling while maintaining RNA-free inference capabilities.
Key Points
- MoPE utilizes knowledge distillation for histology-only inference.
- Improved performance on diverse public benchmarks and breast cancer cohorts.
- Pathway-usage analyses provide insights into model behavior.
- Enables cancer risk modeling with limited RNA profiling availability.
- Maintains RNA-free inference while leveraging molecular information.
Article Content
From source RSS / original summaryarXiv:2606. 02877v1 Announce Type: new Abstract: Integrating transcriptomics and histopathology can improve cancer risk modelling, yet practical use is constrained by the limited availability of RNA profiling in routine settings. Here we introduce Mixture of Pathway Experts (MoPE), a knowledge-distillation framework that reframes multimodal learning as privileged distillation for histology-only inference.
MoPE is motivated by the partial observability between RNA profiles and whole-slide images: histology can capture morphology-linked consequences of certain molecular programmes, but cannot be expected to reconstruct the full transcriptomic state. MoPE encodes RNA-derived pathways and transfers the molecular supervision to pathway-indexed pathology experts through memory-usage alignment.
Across diverse public benchmarks and two independent breast cancer cohorts, MoPE consistently improved WSI-only inference performance relative to baseline methods. Pathway-usage analyses and human-audited visual inspection provide bounded inspection of model behaviour and candidate morphology-linked readouts. These results support pathway-structured privileged distillation as a promising route to using molecular information during training while preserving RNA-free inference.
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