面向医学三维成像的多粒度可观测任务的空间感知自监督
Spatial-Aware Self-Supervision for Medical 3D Imaging with Multi-Granularity Observable Tasks
AI总结：
针对医学三维成像自监督方法缺乏可解释性的问题，提出由三个可观测子任务组成的空间感知自监督方法，通过多粒度空间关系建模，在保持与现有方法相当性能的同时实现学习过程的直观理解。
摘要：
自监督技术在医学可视化任务中的应用日益普遍，这主要归因于其能够缓解医疗领域普遍存在的数据稀缺问题。当前大多数工作受到通用二维视觉领域设计的影响，这些设计缺乏对模型学习三维空间知识过程的直观展示。因此，这些方法在医学可解释性方面往往有所欠缺。我们提出了一种由三个子任务组成的方法，以捕获医学三维成像中空间相关的语义。这些子任务的设计遵循可观测原则以确保可解释性，并尽可能减少由此带来的性能损失。通过利用三维成像中额外维度所提供的增强语义深度，该方法引入了多粒度空间关系建模以维持训练稳定性。实验结果表明，我们的方法能够提供与当前方法相当的性能，同时促进对自监督学习过程的直观理解。
英文摘要：
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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