SCH: Blazing Data Trails: Digital Pathology and Specialist Attention

当在临床玻片中寻找癌症时，病理学家以复杂的方式将他们的注意力转移到玻片周围。这些熟练的注意力转移对病理学家如何做出专业诊断至关重要。这个研究项目旨在了解这些注意力的变化，以便建立一个人工智能（AI）系统，该系统将能够像人类病理学家一样看待幻灯片。然而，建立一个“人工智能专家病理学家”需要大量的数据来学习，就像病理学家需要多年的训练才能成为专家一样。为了给模型提供许多专家注意行为的例子，这对它做出良好的预测至关重要，研究人员将从人类病理学家那里收集大量的注意行为数据集。人类病理学家的行为也将作为对人工智能模型的反馈，使人工智能系统能够模拟和重现人类病理学家如何通过移动他们的注意力来熟练地对幻灯片进行采样。研究人员还将开发人工智能驱动的工具，可以预测专家在幻灯片中会将注意力集中在哪里，从而向人类病理学家提供人工智能病理学家的反馈。其目的是提高人类癌症诊断的准确性，这对改善该国的医疗基础设施至关重要。这项工作也有可能改善医疗人员的组织病理学培训，并导致下一代癌症分类的人工智能模型。通过该项目培训的人工智能科学家将成为构建人工智能工具的专家，这些工具可以理解人类专家的表现并协同提高它。在癌症解释过程中，病理学家基于光标的运动将被创建一个大型数据库，称为注意轨迹。这些数据将从病理学家那里在线收集，这些病理学家在肿瘤手术中切除的淋巴结的全幻灯片图像（WSIs）中搜索转移性癌症。对于每个WSI，还将收集四种“诊断”中的一种：阴性、小转移、中转移或大转移。利用一系列被称为模仿学习的人工智能方法，研究人员将生成病理学家注意轨迹的个性化和群体预测模型，将主动模仿学习应用于真实的人类行为。批处理技术和病理学家在环路学习的注意轨迹也将发展。通过使用多分辨率方法，通过结合计算和人类注意力先验，仅处理wsi的一小部分，期望提高病理分类算法的效率和准确性。最后，将开发基于注意力的诊断辅助工具，建议在更高倍率下检查的区域，供人类病理学家在幻灯片解释期间使用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

When looking for cancer in clinical slides, pathologists move the focus of their attention around the slides in complex ways. These skilled shifts of attention are critical to how pathologists make expert diagnoses. This research program seeks to understand these shifts in attention in order to build an artificial intelligence (AI) system that that will be able to look at a slide the way a human pathologist would. Building an “AI expert pathologist,” however, requires a lot of data for it to learn, just like a pathologist needs years of training to become an expert. In order to provide the model with many examples of expert attention behavior, essential for it to make good predictions, the investigators will collect a large dataset of attention behavior from human pathologists. The human pathologists’ behavior will also serve as feedback to the AI model, enabling the AI system to model and reproduce how the human pathologists expertly sample the slides by moving their focus of attention. The investigators will also build AI-fueled tools that can predict where an expert would have focused their attention in a slide, thereby giving human pathologists feedback from the AI pathologist. The aim is to improve human accuracy of cancer diagnoses, which is paramount to improving the healthcare infrastructure of the country. The work also has the potential to improve histopathology training in medical personnel and to lead to next-generation AI models for cancer classification. The AI scientists trained through this project will be experts in building AI-tools that understand human expert performance and synergistically enhance it. A large database will be created of pathologist’s cursor-based movements during cancer interpretations, referred to as attention trajectories. These will be collected online from pathologists searching for metastatic cancer in Whole Slide Images (WSIs) of lymph nodes that were excised as part of cancer surgeries. For each WSI, one of four “diagnoses” will also be collected: negative, small, medium, or large metastases. Using a family of AI methods called imitation learning, the investigators will generate personalized as well as group prediction models of pathologist attention trajectories, applying Active Imitation Learning to real human behavior. Techniques for batch processing and pathologist-in-the-loop learning of attention trajectories will also be developed. An improvement in the efficiency and accuracy of pathology classification algorithms is expected through use of a multi-resolution approach that only processes small parts of WSIs by combining computational and human attention priors. Lastly, attention-based diagnostic aids that suggest areas to examine at higher magnification will be developed for human pathologists to use during slide interpretationThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.