[2603.28057] Physics-Embedded Feature Learning for AI in Medical Imaging

Computer Science > Machine Learning arXiv:2603.28057 (cs) [Submitted on 30 Mar 2026] Title:Physics-Embedded Feature Learning for AI in Medical Imaging Authors:Pulock Das, Al Amin, Kamrul Hasan, Rohan Thompson, Azubike D. Okpalaeze, Liang Hong View a PDF of the paper titled Physics-Embedded Feature Learning for AI in Medical Imaging, by Pulock Das and 5 other authors View PDF HTML (experimental) Abstract:Deep learning (DL) models have achieved strong performance in an intelligence healthcare setting, yet most existing approaches operate as black boxes and ignore the physical processes that govern tumor growth, limiting interpretability, robustness, and clinical trust. To address this limitation, we propose PhysNet, a physics-embedded DL framework that integrates tumor growth dynamics directly into the feature learning process of a convolutional neural network (CNN). Unlike conventional physics-informed methods that impose physical constraints only at the output level, PhysNet embeds a reaction diffusion model of tumor growth within intermediate feature representations of a ResNet backbone. The architecture jointly performs multi-class tumor classification while learning a latent tumor density field, its temporal evolution, and biologically meaningful physical parameters, including tumor diffusion and growth rates, through end-to-end training. This design is necessary because purely data-driven models, even when highly accurate or ensemble-based, cannot guarantee physically ...