Abstract
We develop a novel transfer learning framework to tackle the challenge of limited training data in image reconstruction problems. The proposed framework consists of two training steps, both of which are formed as bi-level optimizations. In the first step, we train a powerful universal feature-extractor that is capable of learning important knowledge from large, heterogeneous data sets in various domains. In the second step, we train a task-specific domain-adapter for a new target domain or task with only a limited amount of data available for training. Then the composition of the adapter and the universal feature-extractor effectively explores feature which serve as an important component of image regularization for the new domains, and this leads to high-quality reconstruction despite the data limitation issue. We apply this framework to reconstruct under-sampled MR images with limited data by using a collection of diverse data samples from different domains, such as images of other anatomies, measurements of various sampling ratios, and even different image modalities, including natural images. Experimental results demonstrate a promising transfer learning capability of the proposed method.
Abstract (translated)
我们开发了一种新颖的迁移学习框架，旨在解决图像重建问题中训练数据不足的问题。所提出的框架包含两个训练步骤，这两个步骤均采用双层优化形式构建。 在第一步中，我们训练了一个强大的通用特征提取器，它可以从不同领域的大规模异构数据集中学习到重要的知识。第二步中，我们在仅有少量可用训练数据的新目标域或任务上训练一个特定于该任务的领域适配器。然后，适配器与通用特征提取器组合使用，有效地探索出对新领域的图像正则化至关重要的特性，从而即使在数据受限的情况下也能实现高质量的重建。 我们将此框架应用于使用来自不同领域（如其他解剖学部位的图像、各种采样率下的测量值以及包括自然图像在内的不同成像模式）的多样化样本集合来重建欠采样的MRI图像。实验结果表明了该方法具有令人期待的迁移学习能力。
URL
https://arxiv.org/abs/2603.07831
