Deep Orthogonal Fusion for Multimodal Prognostic Biomarker Discovery
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Solution Overview
Problem
Current cancer diagnosis and treatment plans rely on individual analysis of radiology, molecular profiling, and pathology data, failing to leverage the complementary nature of these modalities for more accurate patient outcome predictions.
Innovation Solution
A computer-implemented method using a trained deep learning framework that integrates radiomic, pathology, and molecular datasets to generate a multimodal embedding prediction through loss minimization and embedding fusion, combining radiomic embedding predictions, pathology embedding predictions, and molecular embedding predictions to enhance prognostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data modalities (radiology, molecular profiling, pathology) are analyzed individually, then each modality can be processed with established methods, but the complementary nature of these modalities is not leveraged and prognostic accuracy is limited
Solution Approach 1:
The patent combines multiple independent data modalities (radiology, molecular profiling, pathology) into a unified deep learning framework that processes all modalities simultaneously. The framework integrates features from each modality through embedding layers and fusion mechanisms, creating a comprehensive multimodal biomarker that leverages the complementary information across all data sources to improve prognostic accuracy
Solution Approach 2:
The deep learning framework is designed as a universal system that can process multiple types of data modalities through a common architecture. The framework uses modality-specific embedding layers followed by a shared fusion mechanism, allowing it to handle diverse input types (images, molecular data, pathology data) uniformly while maintaining the unique characteristics of each modality
2Reliability
If a comprehensive multimodal analysis system is implemented, then prognostic accuracy is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The patent segments the multimodal analysis system into distinct modular components: modality-specific embedding layers for each data type, a fusion mechanism for integrating features, and a prediction head for outcome estimation. This segmentation allows each component to be optimized independently while maintaining overall system reliability through their coordinated interaction
Solution Approach 2:
The patent introduces embedding layers as intermediary components that transform raw data from different modalities into a common feature space. These embeddings serve as mediators that enable the fusion mechanism to integrate diverse data types effectively, bridging the gap between heterogeneous input modalities and the prediction output
Data Source
AI summary
A system and method are provided for identifying a multimodal biomarker of a prognostic prediction, using a deep learning framework trained to analyze different modality data, including radiomic image data, pathology image data, and molecular image data to obtain unimodal embedding predictions from those modality data and generate multimodal embedding predictions, through application of a loss minimization and attention-based fusion processes.