BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma
arXiv:2605.26376v1 Announce Type: cross Abstract: Hepatocellular carcinoma (HCC) is biologically heterogeneous, shaped by the interplay between hepatic functional reserve and tumor-related oncologic factors; thus, similar survival outcomes may reflect fundamentally different underlying biological processes. Prognostic modeling in HCC is informed by rich multimodal information from multiparametric MRI and radiology reports from routine clinical practice. Existing prognostic vision-language models (VLMs) learn a single entangled latent representation that blends hepatic and tumor-related factors
The continuous advancements in AI, particularly in vision-language models, are enabling increasingly sophisticated applications in medical diagnostics and prognostics.
Sophisticated medical AI models capable of integrating complex multimodal data can significantly improve diagnostic accuracy and personalize treatment pathways for diseases like HCC.
The development of biologically factorized AI models offers a more nuanced understanding of disease heterogeneity, moving beyond entangled latent representations towards interpretable biological insights.
- · AI healthcare providers
- · Oncology research
- · Patients with HCC
- · Medical imaging companies
- · Traditional prognostic modeling methods
- · Undifferentiated AI diagnostic tools
Improved early diagnosis and personalized treatment for various complex diseases beyond HCC.
Accelerated drug discovery and development due to better phenotypic stratification and understanding of disease mechanisms.
Ethical and regulatory challenges around explainability and bias in highly complex, biologically-informed AI medical systems.
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Read at arXiv cs.LG