OncoVision: A Multimodal Fusion AI System for Cancer Diagnosis and Prognosis Prediction

OncoVision is an open-source end-to-end multimodal AI system that integrates histopathological images, gene expression data (RNA-seq), and clinical information for cancer diagnosis and survival prediction. The system emphasizes interpretability and clinical utility, using technologies like Vision Transformer, representing the cutting-edge direction of multimodal data fusion in the field of precision oncology.

Traditional cancer diagnosis relies on single-modal data (pathological sections, genetic analysis, clinical history), leading to fragmented information flow. Precision oncology requires integrating heterogeneous data to form a comprehensive judgment. OncoVision was developed to address this need, integrating three key data sources: histopathological images (visual features of the tumor microenvironment), RNA-seq data (molecular features), and clinical data (structured information).

The core components of the technical architecture include: 1. Vision Transformer (ViT) for processing pathological images, using self-attention to capture long-range correlations and provide visual explanations; 2. Gene expression encoder to extract low-dimensional prognosis-related signals; 3. Clinical data processed via embedding layers and fully connected networks; 4. Survival analysis models (Cox proportional hazards model, DeepSurv, etc.) to handle censored data Fusion strategies include early, late, intermediate fusion, and attention-guided fusion The tech stack is built on tools like PyTorch, ViT, and scikit-survival.

The system focuses on interpretability: displaying attention areas in pathological images via attention visualization, analyzing key genes/clinical factors through feature importance, and providing case-level personalized explanations Clinical relevance design includes: using real-world data, predicting clinical endpoints like overall survival, and aligning with tumor biology and clinical knowledge.

Application scenarios include: 1. Auxiliary diagnosis: serving as a second opinion for pathologists to address the shortage of experts in resource-poor areas; 2. Prognostic stratification: accurately dividing risk groups to guide treatment (avoid over-treatment for low-risk patients, active intervention for high-risk patients); 3. Biomarker discovery: revealing new prognostic biomarkers through feature importance; 4. Clinical trial screening: identifying patient groups most likely to benefit.

Challenges include: 1. Data challenges: difficulty in data alignment, quality differences (staining/RNA degradation), high cost of survival data annotation; 2. Technical challenges: processing high-resolution pathological images (need for tiling), handling missing modalities, generalization across centers/cancer types; 3. Clinical translation challenges: strict regulatory approval, integration into clinical workflows, improving physician acceptance.

Open-source value: providing methodological references, benchmarking platforms, educational tools, and a foundation for collaborative development Future directions: integrating more modalities (radiological images, proteomics), using federated learning to protect privacy, optimizing real-time inference speed, and expanding to pan-cancer analysis.