发表机构
University of Calgary（卡尔加里大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出结合领域特定（脑龄预测）与通用（图像修复）自监督预文本任务的多任务预训练框架，用于脑部MR图像分割，在三个下游任务上优于单任务预训练和从头训练，展示了其提升模型泛化能力的潜力。
AI 中文摘要
医学图像分析中的一个关键挑战是特定人群和疾病的大型标注数据集的稀缺性。由于深度学习模型严重依赖标注数据，因此需要有效的迁移学习策略来减少对人工标注的依赖。自监督学习已成为开发基础模型的一种有前景的方法，它能够从大规模未标注的医学影像数据集中学习可迁移的特征表示。在本研究中，我们探究了体素级脑龄预测作为一种领域特定的自监督预文本任务，并将其与图像修复（一种广泛使用的非领域特定替代方法）进行比较。我们进一步提出了一种多任务自监督预训练框架，该框架联合优化这两个目标，以学习互补的神经影像表示。预训练模型在三个下游磁共振图像分割任务上进行了评估：多发性硬化病变分割、缺血性卒中病变分割和皮层脑结构分割。总体而言，所提出的多任务预训练框架在大多数实验设置中持续优于单任务预训练模型和从头训练，证明了结合领域特定和通用自监督学习预文本任务对于开发可泛化的神经影像基础模型的益处。代码可用性：本研究中使用的源代码可在此https URL公开获取。
英文摘要
A key challenge in medical image analysis is the scarcity of large annotated datasets for specific populations and diseases. As deep learning models rely heavily on labeled data, effective transfer learning strategies are needed to reduce the dependence on manual annotations. Self-supervised learning has emerged as a promising approach for developing foundation models by enabling the learning of transferable feature representations from large-scale unlabeled medical imaging datasets. In this study, we investigate voxel-level brain age prediction as a domain-specific self-supervised pretext task and compare it with image inpainting, a widely used non-domain-specific alternative. We further propose a multitask self-supervised pretraining framework that jointly optimizes both objectives to learn complementary neuroimaging representations. The pretrained models are evaluated on three downstream magnetic resonance image segmentation tasks: multiple sclerosis lesion segmentation, ischemic stroke lesion segmentation, and cortical brain structure segmentation. Overall, the proposed multitask pretraining framework consistently outperformed the single-task pretrained models and training from scratch across most experimental settings, demonstrating the benefit of combining domain-specific and general self-supervised learning pretext tasks for the development of generalizable neuroimaging foundation models.\ Code Availability: The source code used in this study is publicly available at https://github.com/TasneemN/Combining-General-and-Domain-Specific-Pretext-Tasks-for-Brain-MR-Image-Segmentation/
Comments7 figures, 5 tables
