Hierarchical Prototype-based Domain Priors for Multiple Instance Learning in Multimodal Histopathology Analysis

2026-04-26

AI Summary

Existing multiple instance learning (MIL) approaches treat whole-slide images as unstructured collections of image patches, thereby neglecting the morphological semantics and spatial geometric relationships inherent in tissue architecture. This limitation renders them susceptible to background noise and misaligned with clinical diagnostic reasoning. To address this, this work proposes the HPDP framework, which introduces a Morphology-Anchored Prototype System (MAPS) to explicitly model histological structural semantics, incorporates sinusoidal positional encoding (SPE) to capture spatial geometry, and designs a Hierarchical Cross-Modal Alignment (HCMA) module that leverages pathology descriptions generated by large language models to achieve image–text semantic alignment. Evaluated across seven cancer cohorts, the proposed method significantly improves diagnostic accuracy, robustness, and interpretability, outperforming current state-of-the-art approaches.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Digital pathology has fundamentally altered diagnostic workflows by enabling the computational analysis of gigapixel Whole Slide Images (WSIs), yet effectively deciphering their complex tumor microenvironments remains a formidable challenge. Existing Multiple Instance Learning (MIL) frameworks typically treat Whole Slide Images as unstructured bags of patches, discarding critical morphological semantics and spatial geometry. This lack of inductive bias often leads to overfitting on background noise and fails to align visual features with high-level diagnostic knowledge. To overcome these limitations, we propose the Hierarchical Prototype-based Domain Priors (HPDP) framework, a unified multimodal approach for joint histopathology diagnosis and prognosis. HPDP mitigates the data-driven "black box" issue by introducing a Morphologically Anchored Prototype System (MAPS), which anchors learning to interpretable morphological clusters, and a Sinusoidal Positional Encoder (SPE) to explicitly model tissue architecture. Furthermore, we bridge the semantic gap via a Hierarchical Cross-Modal Alignment (HCMA) module, using Large Language Model (LLM)-generated descriptions to contextually refine visual representations. Extensive experiments across seven cancer cohorts demonstrate that HPDP consistently achieves state-of-the-art performance with superior robustness and interpretability.

Problem

• Multiple Instance Learning
• Whole Slide Images
• morphological semantics
• spatial geometry
• diagnostic knowledge

Innovation

• Hierarchical Prototype
• Morphologically Anchored Prototype System
• Sinusoidal Positional Encoder
• Hierarchical Cross-Modal Alignment
• Multiple Instance Learning