通过多粒度空间任务提升3D医学影像自监督学习的可解释性
Spatial-Aware Self-Supervision for Medical 3D Imaging with Multi-Granularity Observable Tasks
- 设计三个可观测的子任务，捕捉3D影像中的空间语义
- 在保持性能不降的前提下，实现训练过程的直观可解释
- 适合关注医学影像可解释性的研究者与临床应用开发者
自监督学习在医疗可视化任务中日益普及，主要因其能缓解医疗领域数据稀缺的问题。现有方法多借鉴通用2D视觉领域的设计，缺乏对模型学习3D空间知识过程的直观展示，导致医学可解释性不足。本文提出一种包含三个子任务的方法，以捕捉医学3D影像中的空间相关语义。这些任务的设计遵循可观测原则，确保可解释性的同时最大限度减少性能损失。借助3D影像额外维度带来的语义深度，该方法引入多粒度空间关系建模，以维持训练稳定性。实验表明，该方法性能可媲美现有主流方法，同时使自监督学习过程更直观易懂。
原文摘要 · Abstract (English)
The application of self-supervised techniques has become increasingly prevalent within medical visualization tasks, primarily due to its capacity to mitigate the data scarcity prevalent in the healthcare sector. The majority of current works are influenced by designs originating in the generic 2D visual domain, which lack the intuitive demonstration of the model's learning process regarding 3D spatial knowledge. Consequently, these methods often fall short in terms of medical interpretability. We propose a method consisting of three sub-tasks to capture the spatially relevant semantics in medical 3D imaging. Their design adheres to observable principles to ensure interpretability, and minimize the performance loss caused thereby as much as possible. By leveraging the enhanced semantic depth offered by the extra dimension in 3D imaging, this approach incorporates multi-granularity spatial relationship modeling to maintain training stability. Experimental findings suggest that our approach is capable of delivering performance that is on par with current methodologies, while facilitating an intuitive understanding of the self-supervised learning process.
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