The medical image processing field often encounters the critical issue of scarce annotated data. Transfer learning has emerged as a solution, yet how to select an adequate source task and effectively transfer the knowledge to the target task remains challenging. To address this, we propose a novel sequential transfer scheme with a task affinity metric tailored for medical images. Considering the characteristics of medical image segmentation tasks, we analyze the image and label similarity between tasks and compute the task affinity scores, which assess the relatedness among tasks. Based on this, we select appropriate source tasks and develop an effective sequential transfer strategy by incorporating intermediate source tasks to gradually narrow the domain discrepancy and minimize the transfer cost. Thereby we identify the best sequential transfer path for the given target task. Extensive experiments on three MRI medical datasets, FeTS 2022, iSeg-2019, and WMH, demonstrate the efficacy of our method in finding the best source sequence. Compared with directly transferring from a single source task, the sequential transfer results underline a significant improvement in target task performance, achieving an average of 2.58% gain in terms of segmentation Dice score, notably, 6.00% for FeTS 2022. Code is available at the git repository.

翻译：医学图像处理领域常面临标注数据稀缺的关键问题。迁移学习已成为一种解决方案，然而如何选择适当的源任务并将知识有效迁移至目标任务仍然具有挑战性。为此，我们提出一种新颖的序列迁移方案，并设计了一种针对医学图像的任务亲和度度量方法。考虑到医学图像分割任务的特点，我们分析任务间的图像与标签相似性，并计算任务亲和度得分以评估任务间的相关性。基于此，我们选择合适的源任务，并通过引入中间源任务来制定有效的序列迁移策略，以逐步缩小领域差异并最小化迁移成本。由此，我们为给定的目标任务确定最佳序列迁移路径。在三个MRI医学数据集（FeTS 2022、iSeg-2019和WMH）上进行的大量实验证明了我们方法在寻找最佳源序列方面的有效性。与直接从单一源任务迁移相比，序列迁移结果突显了目标任务性能的显著提升，在分割Dice分数上平均获得2.58%的提高，其中FeTS 2022数据集上尤为显著，提升了6.00%。代码可在git仓库中获取。