Unified Modeling Enhanced Multimodal Learning for Precision Neuro-Oncology

TL;DR

This paper introduces a novel unified modeling framework that enhances multimodal learning by effectively integrating histology images and genomics data, improving glioma diagnosis and prognosis in neuro-oncology.

Contribution

The study presents a hierarchical attention-based UMEML framework with prototype clustering and registration mechanisms for better multimodal integration, addressing modality imbalance and gaps.

Findings

Outperforms previous methods in glioma diagnosis.

Improves prognosis accuracy in neuro-oncology.

Enhances cross-modal feature robustness.

Abstract

Multimodal learning, integrating histology images and genomics, promises to enhance precision oncology with comprehensive views at microscopic and molecular levels. However, existing methods may not sufficiently model the shared or complementary information for more effective integration. In this study, we introduce a Unified Modeling Enhanced Multimodal Learning (UMEML) framework that employs a hierarchical attention structure to effectively leverage shared and complementary features of both modalities of histology and genomics. Specifically, to mitigate unimodal bias from modality imbalance, we utilize a query-based cross-attention mechanism for prototype clustering in the pathology encoder. Our prototype assignment and modularity strategy are designed to align shared features and minimizes modality gaps. An additional registration mechanism with learnable tokens is introduced to enhance cross-modal feature integration and robustness in multimodal unified modeling. Our experiments demonstrate that our method surpasses previous state-of-the-art approaches in glioma diagnosis and prognosis tasks, underscoring its superiority in precision neuro-Oncology.