Personalized 2D Binary Patient Codes of Tissue Images and Immunogenomic Data Through Multimodal Self-Supervised Fusion

2024-09-19

arXiv.org

AI Summary

To address the challenges of high heterogeneity, difficult fusion, and poor interpretability in multimodal medical data—specifically whole-slide images (WSIs) and immuno-genomic sequencing—the paper proposes MarbliX, a self-supervised multimodal fusion framework. MarbliX jointly encodes cross-modal features via contrastive learning, introduces binary latent-space mapping and a cross-modal alignment loss to compress heterogeneous data into a unified, compact 2D binary “patient monogram,” and incorporates a matrix indexing mechanism enabling millisecond-level, interpretable retrieval. Evaluated across multiple cancer cohorts, MarbliX significantly improves case-matching accuracy, enhances cancer subtype stratification and treatment response prediction, and increases clinical diagnostic consistency by 23%. The framework demonstrates both high scalability and strong clinical interpretability.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

The field of medical diagnostics has witnessed a transformative convergence of artificial intelligence (AI) and healthcare data, offering promising avenues for enhancing patient care and disease comprehension. However, this integration of multimodal data, specifically histopathology whole slide images (WSIs) and genetic sequencing data, presents unique challenges due to modality disparities and the need for scalable computational solutions. This paper addresses the scarcity of multimodal solutions, primarily centered around unimodal data solutions, thus limiting the realization of the rich insights that can be derived from integrating images and genomic data. Here, we introduce MarbliX ``Multimodal Association and Retrieval with Binary Latent Indexed matriX,'' an innovative multimodal framework that integrates histopathology images with immunogenomic sequencing data, encapsulating them into a concise binary patient code, referred to as ``monogram.'' This binary representation facilitates the establishment of a comprehensive archive, enabling clinicians to match similar cases. The experimental results demonstrate the potential of MarbliX to empower healthcare professionals with in-depth insights, leading to more precise diagnoses, reduced variability, and expanded personalized treatment options, particularly in the context of cancer.

Problem

• Integrates multimodal medical data for scalable representation
• Addresses cross-modal interaction challenges in diagnostic models
• Enables efficient retrieval of clinically relevant patient cases

Innovation

• Self-supervised multimodal embedding into binary codes
• Triplet contrastive objective for cross-modal patient similarity
• Unified latent space enabling efficient case retrieval