Standardizing Medical Images at Scale for AI
Deep learning has achieved remarkable success in medical image analysis, yet its performance remains highly sensitive to the heterogeneity of clinical data. Differences in imaging hardware, staining protocols, and acquisition conditions produce substantial domain shifts that degrade model generalization across institutions. Here we present a physics-based data preprocessing framework based on the PhyCV (Physics-Inspired Computer Vision) family of algorithms, which standardizes medical images through deterministic transformations derived from optical physics. The framework models images as spatially varying optical fields that undergo a virtual diffractive propagation followed by coherent ...
| Authors | Callen MacPhee, Yiming Zhou, Koichiro Kishima et al. |
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| Primary category | eess.IV |
| Published | 16 March 2026 |
| Last updated at source | 16 March 2026 |
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