Test-Time Adaptation via Orthogonal Meta-Learning for Medical Imaging

Abstract

Deep learning (DL) models, which have significantly promoted medical imaging, typically assume that training and testing data come from the same domain and distribution. However, these models struggle with unseen testing variations, like different imaging scanners or protocols, leading to suboptimal results from distribution mismatches between training and testing data. Despite extensive research, the issue of distribution mismatch in DL-based medical imaging has been largely overlooked in current literature. To improve the performance with mismatched testing data, this article proposes an orthogonal meta-learning (OML) framework for test-time adaptation (TTA) in medical imaging. Specifically, during training, we develop supervised meta-training reconstruction tasks to guide the self-supervised meta-testing task. Additionally, we introduce an orthogonal learning strategy to enforce orthogonality of pretrained parameters during training, which accelerates convergence during TTA and enhances performance. During the testing stage, the fine-tuned meta-learned parameters effectively reconstruct new, unseen testing data. Extensive experiments on magnetic resonance imaging and computed tomography datasets were conducted to validate our method’s effectiveness against other state-of-the-art methods, including supervised ones, in various mismatch scenarios.