让你的朋友更近，让敌人更远：3D放射学图像中空间先验的去偏对比学习
Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images
摘要：
空间属性的理解是有效的3D放射学图像分析的核心，其中基于作物的学习是事实标准。给定图像块，其核心空间属性（例如，位置和取向）通过固有的解剖学特征提供关于预期对象尺寸、外观和结构的有用先验。空间对应，特别是，可以有效地衡量图像间区域之间的语义相似性，而它们的近似提取不需要注释或霸道的计算成本。然而，最近的3D对比学习方法要么忽略对应关系，要么未能最大限度地利用它们。为此，我们提出了一个可扩展的3D对比框架（Spade，用于空间去偏），该框架利用提取的对应关系来选择更有效的正样本和负样本进行表示学习。我们的方法学习全局不变和局部等变表示，并考虑到下游分割。我们还提出了单独的选择策略，为全球和本地的范围，定制各自的代表性要求。与最新的最先进方法相比，Spade在三个下游分割任务（CT腹部器官、CT心脏、MR心脏）上显示出显著的改进。
英文摘要：
Understanding of spatial attributes is central to effective 3D radiology image analysis where crop-based learning is the de facto standard. Given an image patch, its core spatial properties (e.g., position & orientation) provide helpful priors on expected object sizes, appearances, and structures through inherent anatomical consistencies. Spatial correspondences, in particular, can effectively gauge semantic similarities between inter-image regions, while their approximate extraction requires no annotations or overbearing computational costs. However, recent 3D contrastive learning approaches either neglect correspondences or fail to maximally capitalize on them. To this end, we propose an extensible 3D contrastive framework (Spade, for Spatial Debiasing) that leverages extracted correspondences to select more effective positive & negative samples for representation learning. Our method learns both globally invariant and locally equivariant representations with downstream segmentation in mind. We also propose separate selection strategies for global & local scopes that tailor to their respective representational requirements. Compared to recent state-of-the-art approaches, Spade shows notable improvements on three downstream segmentation tasks (CT Abdominal Organ, CT Heart, MR Heart).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
