[论文解读] Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology

Andreas Holzinger, Bernd Malle+5 more|arXiv (Cornell University)|Dec 18, 2017

AI in cancer detection参考文献 32被引用 69

一句话总结

The paper outlines a research agenda for integrating AI/ML with human pathologists in digital pathology, emphasizing explainability, data integration, and educational as well as clinical workflows to create an “augmented pathologist.”

ABSTRACT

Digital pathology is not only one of the most promising fields of diagnostic medicine, but at the same time a hot topic for fundamental research. Digital pathology is not just the transfer of histopathological slides into digital representations. The combination of different data sources (images, patient records, and *omics data) together with current advances in artificial intelligence/machine learning enable to make novel information accessible and quantifiable to a human expert, which is not yet available and not exploited in current medical settings. The grand goal is to reach a level of usable intelligence to understand the data in the context of an application task, thereby making machine decisions transparent, interpretable and explainable. The foundation of such an "augmented pathologist" needs an integrated approach: While machine learning algorithms require many thousands of training examples, a human expert is often confronted with only a few data points. Interestingly, humans can learn from such few examples and are able to instantly interpret complex patterns. Consequently, the grand goal is to combine the possibilities of artificial intelligence with human intelligence and to find a well-suited balance between them to enable what neither of them could do on their own. This can raise the quality of education, diagnosis, prognosis and prediction of cancer and other diseases. In this paper we describe some (incomplete) research issues which we believe should be addressed in an integrated and concerted effort for paving the way towards the augmented pathologist.

研究动机与目标

• 推动在数字病理学中使用 AI/ML 的需求，以提升人类专家的能力，而非取代临床医生。
• 提出一个整合的以人为本的方法，结合人机交互（HCI）与知识发现/数据挖掘（KDD），以实现可解释的洞察。
• 确定以数据为中心的前提条件，如全组织切片成像（WSI）格式的标准化以及注释/元数据。
• 讨论跨图像、EPRs 和 *omics 数据的数据整合，以揭示新的生物标志物和诊断洞察。
• 突显方法学方向，包括可解释的深度学习、基于图的方法，以及拓扑数据挖掘，以支持可解释性。

提出的方法

• 描述一个机器辅助病理学的工作流程，包括假设形成、特征检测/分类以及风险预测。
• 讨论多分辨率分析以及使用图像金字塔来管理 TB 级别的切片数据。
• 评估可解释深度学习的方法，包括诸如反卷积网络之类的可视化技术，将特征与输入空间相关联。
• 建议基于图论和概率的方法，将异构数据（图像、EHRs、*omics）连接起来以实现可解释的推理。
• 提出拓扑数据挖掘概念，用于处理流形结构和基于邻近性的医疗图像解释。

实验结果

研究问题

• RQ1 在使基于 AI 的病理学可解释并在临床实践中可用方面，存在哪些关键挑战和研究问题？
• RQ2 影像、临床记录和 *omics 数据的整合如何支持增强型病理学家？
• RQ3 哪些 AI/ML 与可视化策略可以使数字病理中的深度学习模型更加透明和值得信赖？
• RQ4 基于图和拓扑数据挖掘的方法如何加强病理学中的跨模态推理？

主要发现

• AI/ML 可以在假设形成、特征检测和预后等诊断工作流程中提供增益，潜在地提升质量和教育效果。
• WSI 数据极大（例如每张图像为 16 Gigapixels）并且需要多分辨率分析与数据管理策略。
• 可解释的 DL 方法（例如 deconvnet 可视化）能够提供跨层学习特征的洞察，有助于可解释性。
• 基于图的表示为将图像区域与异质数据源连接起来以进行整合分析提供了一条路径。
• 数据标准化和跨厂商可视化范式对互操作性和教育至关重要。