Although many studies have successfully applied transfer learning to medical image segmentation, very few of them have investigated the selection strategy when multiple source tasks are available for transfer. In this paper, we propose a prior knowledge guided and transferability based framework to select the best source tasks among a collection of brain image segmentation tasks, to improve the transfer learning performance on the given target task. The framework consists of modality analysis, RoI (region of interest) analysis, and transferability estimation, such that the source task selection can be refined step by step. Specifically, we adapt the state-of-the-art analytical transferability estimation metrics to medical image segmentation tasks and further show that their performance can be significantly boosted by filtering candidate source tasks based on modality and RoI characteristics. Our experiments on brain matter, brain tumor, and white matter hyperintensities segmentation datasets reveal that transferring from different tasks under the same modality is often more successful than transferring from the same task under different modalities. Furthermore, within the same modality, transferring from the source task that has stronger RoI shape similarity with the target task can significantly improve the final transfer performance. And such similarity can be captured using the Structural Similarity index in the label space.
翻译:虽然许多研究成功地应用了将学习转移到医学图像分割方面的学习,但其中极少数研究在有多种来源任务可供转移时对选择战略进行了调查。在本文件中,我们提出一个先前的知识指导和可转让性框架,以在收集大脑图像分割任务中选择最佳源任务,提高特定目标任务中的转移学习绩效。框架包括模式分析、区域利益关系分析和可转让性估计,以便源任务选择可以一步一步地完善。具体地说,我们调整了最先进的可转让性分析估计指标,使之适应医疗图像分割任务,并进一步表明根据模式和罗伊特性过滤候选源任务可以大大提升其绩效。我们在脑物质、脑肿瘤和白物质超密度分割方面的实验显示,在同一模式下的不同任务转移往往比在不同模式下从同一任务转移更成功。此外,在同一模式下,从更强大的源任务中转移RoI与目标任务相似的可大大改进最终转移性能。这种相似性可以通过结构相似的索引标签在空间上捕捉到类似性。