Probing, Fusion, and Trustworthiness: A Systematic Evaluation of Foundation Model Representations for Multimodal Cancer Analysis
arXiv:2606.17115v1 Announce Type: cross Abstract: Foundation models (FMs) have emerged as powerful representation extractors for medical data, yet their generalizability to datasets under distribution shift remains underexplored. This work systematically evaluates FM-based representations on a suite of computational pathology tasks across two real-world commercial cohorts, IH-BC and IH-NSCLC, drawn from the licensed in-house (IH) oncology dataset. The analysis focuses on two modalities, whole-slide images and transcriptomic profiles, drawn from the IH multimodal data. We first benchmark unimod
The rapid advancement and adoption of foundation models in various domains are naturally leading to their application and rigorous evaluation in medical data analysis.
This work validates the utility and generalizability of foundation models for complex multimodal medical tasks, potentially accelerating AI integration in clinical diagnostics and drug discovery.
The systematic evaluation framework provides stronger evidence for the reliability of foundation models in medical contexts, potentially shifting development focus towards more trustworthy AI in healthcare.
- · AI in healthcare developers
- · oncology research
- · computational pathology companies
- · pharmaceutical industry
- · traditional pathology methods
- · medical AI companies with less robust models
- · developers of non-generalizable AI solutions
Foundation models become a standard tool for multimodal cancer analysis, improving diagnostic accuracy and research efficiency.
Increased investment and regulatory scrutiny will focus on the trustworthiness and generalizability of AI for medical applications.
The success in oncology could catalyze the broader application of FMs across other complex diseases, leading to more personalized medicine approaches.
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Read at arXiv cs.AI