arXiv:2605.25348v1 Announce Type: cross Abstract: Low-dose computed tomography (LDCT) reconstruction faces a critical tradeoff between reconstruction quality and resource requirements. While recent deep learning methods achieve state-of-the-art performance, they typically rely on over 500,000 parameters trained on large-scale datasets exceeding 35,000 scans. This work investigates whether graph-based regularization can provide meaningful noise reduction under strict resource constraints. We propose Deep Graph Laplacian Regularization (Deep GLR), integrating quadratic graph regularization into
The continuous push for more efficient and less resource-intensive AI methods, especially in computationally demanding fields like medical imaging, drives innovations like Deep GLR.
Reducing the parameter count and data requirements for deep learning in CT reconstruction democratizes access to state-of-the-art medical imaging and lowers the computational barrier for deployment.
The reliance on massive datasets and parameter-heavy models for high-quality medical image reconstruction could decrease, making advanced AI imaging more accessible and efficient.
- · Hospitals in resource-constrained environments
- · Medical AI software developers
- · Patients needing CT scans
- · Edge computing platforms
- · Developers focused solely on large, data-intensive models
- · Companies with high data storage and training costs
More widespread adoption of AI-enhanced CT reconstruction in clinical settings due to lower resource demands.
Accelerated development of other parameter-efficient AI models across various imaging modalities and medical applications.
Increased competition in medical AI with new entrants leveraging efficient models, potentially driving down costs or improving quality of care globally.
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Read at arXiv cs.LG