通过2.5D多实例学习对3D病理数据进行分诊以指导病理学家评估
Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments
AI总结：
本文提出CARP3D深度学习方法，通过2.5D多实例学习自动识别3D活检中的高风险2D切片，在前列腺癌风险分层中AUC达90.4%，可辅助病理学家高效审查。
摘要：
当前临床实践中，病理学家仅评估从3D体积组织中切取的数量有限的薄2D组织切片，这阻碍了基于人体组织活检的准确患者诊断。非破坏性3D病理学的最新进展，如开放式顶板光片显微镜，能够对空间异质性组织形态进行全面成像，为提高诊断确定性提供了可行性。3D病理学临床采用的一条潜在早期途径是依靠病理学家基于查看3D数据集中熟悉的2D H&E样图像切片来做出最终诊断。然而，人工检查海量3D病理数据集是不可行的。为解决这一问题，我们提出了CARP3D，一种深度学习分诊方法，可自动识别3D体积活检中风险最高的2D切片，从而使病理学家能够进行时间高效的审查。对于活检中的给定切片，我们通过对每个切片内的2D图像块进行基于注意力的聚合来估计其风险，然后对相邻切片进行池化以计算上下文感知的2.5D风险评分。在前列腺癌风险分层中，CARP3D在切片分诊方面实现了90.4%的曲线下面积（AUC），优于依赖2D切片独立分析的方法（AUC=81.3%）。这些结果表明，整合额外的深度上下文增强了模型的判别能力。总之，CARP3D有潜力通过在大体积3D病理数据集中准确分诊高风险切片来改进病理学家的诊断。
英文摘要：
Accurate patient diagnoses based on human tissue biopsies are hindered by current clinical practice, where pathologists assess only a limited number of thin 2D tissue slices sectioned from 3D volumetric tissue. Recent advances in non-destructive 3D pathology, such as open-top light-sheet microscopy, enable comprehensive imaging of spatially heterogeneous tissue morphologies, offering the feasibility to improve diagnostic determinations. A potential early route towards clinical adoption for 3D pathology is to rely on pathologists for final diagnosis based on viewing familiar 2D H&E-like image sections from the 3D datasets. However, manual examination of the massive 3D pathology datasets is infeasible. To address this, we present CARP3D, a deep learning triage approach that automatically identifies the highest-risk 2D slices within 3D volumetric biopsy, enabling time-efficient review by pathologists. For a given slice in the biopsy, we estimate its risk by performing attention-based aggregation of 2D patches within each slice, followed by pooling of the neighboring slices to compute a context-aware 2.5D risk score. For prostate cancer risk stratification, CARP3D achieves an area under the curve (AUC) of 90.4% for triaging slices, outperforming methods relying on independent analysis of 2D sections (AUC=81.3%). These results suggest that integrating additional depth context enhances the model's discriminative capabilities. In conclusion, CARP3D has the potential to improve pathologist diagnosis via accurate triage of high-risk slices within large-volume 3D pathology datasets.
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。
