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ARXIV:2603.28057 · MEDICAL AI · SUBMITTED 31 MAR · 20:53 UTC · FRESHNESS STALE
ARXIV:2603.28057MEDICAL AISUBMITTED 31 MAR · 20:53 UTCFRESHNESS STALEPulock Das · Al Amin · Kamrul Hasan · Rohan Thompson · Azubike D. Okpalaeze · Liang Hong · arXiv
A physics-embedded deep learning framework for medical imaging that integrates tumor growth dynamics for more interpretable and trustworthy AI.
Opportunity summary
Pain A physics-embedded deep learning framework for medical imaging that integrates tumor growth dynamics for more interpretable and trustworthy AI.
Evidence 19 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A physics-embedded deep learning framework for medical imaging that integrates tumor growth dynamics for more interpretable and trustworthy AI. To address this limitation, we propose PhysNet, a physics-embedded DL framework that integrates tumor growth…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experimental results on a large brain MRI dataset demonstrate that PhysNet outperforms multiple state-of-the-art DL baselines, including MobileNetV2, VGG16, VGG19, and ensemble models, achieving…
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A physics-embedded deep learning framework for medical imaging that integrates tumor growth dynamics for more interpretable and trustworthy AI.
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10.48550/arXiv.2603.28057A physics-embedded deep learning framework for medical imaging that integrates tumor growth dynamics for more interpretable and trustworthy AI.
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 consistent predictions or provide insight into tumor behavior. Experimental results on a large brain MRI dataset demonstrate that PhysNet outperforms multiple state-of-the-art DL baselines, including MobileNetV2, VGG16, VGG19, and ensemble models, achieving superior classification accuracy and F1-score. In addition to improved performance, PhysNet produces interpretable latent representations and learned bio-physical parameters that align with established medical knowledge, highlighting physics-embedded representation learning as a practical pathway toward more trustworthy and clinically meaningful medical AI systems.
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unverified19 refs; 3 sources; 50% coverage.
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PROBLEM
A physics-embedded deep learning framework for medical imaging that integrates tumor growth dynamics for more interpretable and trustworthy AI. To address this limitation, we propose PhysNet, a physics-embedded DL framework that integrates tumor growth dynamics directly into the...
METHOD
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...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experimental results on a large brain MRI dataset demonstrate that PhysNet outperforms multiple state-of-the-art DL baselines, including MobileNetV2, VGG16, VGG19, and ensemble models, achieving superior...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
PhysNet embeds a reaction diffusion model of tumor growth within intermediate feature representations of a ResNet backbone.
Explicitly stated as the core methodological contribution in both the abstract and methodology section.
partial
PhysNet achieves 96.8% accuracy and 96.2% F1-score, outperforming all baselines by significant margins.
Directly stated with specific performance metrics in the results section.
partial
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.
Directly stated as a key differentiator from prior work in the abstract and introduction.
partial
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.
Explicitly stated as a key capability and result in the abstract and methodology.
partial
This design is necessary because purely data-driven models, even when highly accurate or ensemble-based, cannot guarantee physically consistent predictions or provide insight into tumor behavior.
Directly stated as a limitation of existing approaches that motivates the work.
partial
This allows the network to initially focus on classification (when L_cls is high) and progressively enforce physics constraints as classification stabilizes.
Explicitly described in the methodology section with a provided equation.
partial
For each input MRI image, two augmented views are generated... The two views are treated as a pseudo-temporal pair for enforcing temporal consistency.
Clearly described in the training algorithm section, though the term 'pseudo-temporal pair' is implied.
partial
Although evaluated on 2D MRI slices, the method directly extends to 3D volumetric data.
Directly stated in the concluding analysis, though not demonstrated in the reported experiments.
partial
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A physics-embedded deep learning framework for medical imaging that integrates tumor growth dynamics for more interpretable and trustworthy AI.
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Medical AI
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19 refs / 3 sources / 50% coverage
stale
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Evidence
19 references, 3 sources, 50% evidence coverage.
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