An incremental algorithm for non-convex AI-enhanced medical image processing

• URL: http://arxiv.org/abs/2505.08324v1
• Date: Tue, 13 May 2025 08:03:14 GMT
• Title: An incremental algorithm for non-convex AI-enhanced medical image processing
• Authors: Elena Morotti,
• Abstract summary: We propose a hybrid framework that integrates model-based optimization and deep learning-based methods to solve inverse problems in medical imaging.<n>We show that incDG outperforms both conventional iterative solvers and deep learning-based methods, achieving superior accuracy and stability.<n>We conclude that incDG does not significantly degrade performance, making it a practical and powerful tool for solving non deblurring problems in imaging beyond.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Solving non-convex regularized inverse problems is challenging due to their complex optimization landscapes and multiple local minima. However, these models remain widely studied as they often yield high-quality, task-oriented solutions, particularly in medical imaging, where the goal is to enhance clinically relevant features rather than merely minimizing global error. We propose incDG, a hybrid framework that integrates deep learning with incremental model-based optimization to efficiently approximate the $\ell_0$-optimal solution of imaging inverse problems. Built on the Deep Guess strategy, incDG exploits a deep neural network to generate effective initializations for a non-convex variational solver, which refines the reconstruction through regularized incremental iterations. This design combines the efficiency of Artificial Intelligence (AI) tools with the theoretical guarantees of model-based optimization, ensuring robustness and stability. We validate incDG on TpV-regularized optimization tasks, demonstrating its effectiveness in medical image deblurring and tomographic reconstruction across diverse datasets, including synthetic images, brain CT slices, and chest-abdomen scans. Results show that incDG outperforms both conventional iterative solvers and deep learning-based methods, achieving superior accuracy and stability. Moreover, we confirm that training incDG without ground truth does not significantly degrade performance, making it a practical and powerful tool for solving non-convex inverse problems in imaging and beyond.

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