arXiv:2606.19966v1 Announce Type: cross Abstract: Whole-slide images (WSIs) are widely used for computational cancer prognosis. However, most existing methods primarily focus on in-domain performance and fail to generalize across clinical centers. This limitation stems from their reliance on pixel-derived representations that are highly susceptible to domain-specific artifacts caused by staining protocols and scanner hardware. We hypothesize that high-level pathology semantics, such as tumor grade and micro-environmental architecture, provide a domain-invariant semantic representation that mir
The proliferation of AI in medical imaging has highlighted generalization issues stemming from data variance, making domain-robust solutions critical for clinical adoption.
This research addresses a fundamental limitation in AI's application to medical diagnostics—poor generalization across different clinical environments, which is crucial for equitable and reliable healthcare.
The proposed method could lead to more reliable and universally applicable AI models for cancer prognosis, reducing disparities caused by varying hospital equipment and procedures.
- · AI-powered medical diagnostics companies
- · Healthcare providers in diverse settings
- · Cancer patients globally
- · AI models reliant solely on pixel-derived features
- · Companies with proprietary, non-generalizable AI solutions
Improved accuracy and reliability of AI in cancer prognosis across different hospitals and regions.
Accelerated adoption of AI in pathology due to increased trust and wider applicability.
New standards for AI model robustness and generalization in medical imaging, influencing regulatory frameworks.
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