All-In-One Medical Image Restoration via Task-Adaptive Routing
Zhiwen Yang, Haowei Chen, Ziniu Qian, Yang Yi, Hui Zhang, Dan Zhao,, Bingzheng Wei, Yan Xu

TL;DR
This paper introduces a universal model for multiple medical image restoration tasks that uses task-adaptive routing to mitigate task interference, achieving state-of-the-art results across MRI, CT, and PET imaging tasks.
Contribution
The paper proposes a novel task-adaptive routing strategy that enables a single model to effectively handle diverse MedIR tasks by reducing task interference.
Findings
Achieves state-of-the-art performance on MRI super-resolution, CT denoising, and PET synthesis.
Effective in both single-task and all-in-one training settings.
Demonstrates improved generalizability across different MedIR tasks.
Abstract
Although single-task medical image restoration (MedIR) has witnessed remarkable success, the limited generalizability of these methods poses a substantial obstacle to wider application. In this paper, we focus on the task of all-in-one medical image restoration, aiming to address multiple distinct MedIR tasks with a single universal model. Nonetheless, due to significant differences between different MedIR tasks, training a universal model often encounters task interference issues, where different tasks with shared parameters may conflict with each other in the gradient update direction. This task interference leads to deviation of the model update direction from the optimal path, thereby affecting the model's performance. To tackle this issue, we propose a task-adaptive routing strategy, allowing conflicting tasks to select different network paths in spatial and channel dimensions,…
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Taxonomy
TopicsMedical Imaging Techniques and Applications · Medical Image Segmentation Techniques · Advanced Image Processing Techniques
MethodsFocus
