数字病理中的半自动化质量保证：图块分类方法
Semi-Automated Quality Assurance in Digital Pathology: Tile Classification Approach
AI总结：
针对数字病理质量保证方法不足的问题，提出基于图块分类的AI伪影筛查算法，通过混合单伪影模型与多示例模型优化检测，减少人工审核成本。
摘要：
质量保证是数字病理中至关重要但探索不足的领域，即便微小的伪影也可能产生重大影响，且已被证实会对AI诊断模型的性能造成负面影响。当前实践中，经过培训的工作人员会在将数字化切片交付病理医生用于诊断前，对其进行人工审核。传统图像处理方法为数字病理切片的伪影检测奠定了基础，但现有工具未利用深度学习技术，而深度学习有望提升检测准确率与可扩展性。尽管已有相关技术进展，数字病理的质量保证方法仍然有限，存在创新空白。\n我们提出一种AI算法，通过分析图块并将其归类为10种预定义伪影类型之一或背景，来筛查数字病理切片。该算法可识别并定位伪影，生成高亮显示感兴趣区域的图谱。通过引导人工操作人员关注受伪影影响的特定图块，该算法减少了人工审核整张切片质量问题所需的时间与精力。\n研究从内部档案和The Cancer Genome Atlas中选取了133张全切片图像，使用内部开发的软件ZAPP（梅奥诊所，佛罗里达州杰克逊维尔）标注了10种伪影。针对不同图块尺寸和放大倍率下的多种模型开展了消融研究，最终选定InceptionResNet模型。研究先训练并测试了单伪影模型，随后构建了一个有限多示例模型，纳入了协同表现良好的伪影（震颤、折叠和钢笔标记）。基于研究结果，我们提出一种混合式伪影筛查设计，结合单伪影二分类模型与多示例模型，以优化各类伪影的检测效果。
英文摘要：
Quality assurance is a critical but underexplored area in digital pathology, where even minor artifacts can have significant effects. Artifacts have been shown to negatively impact the performance of AI diagnostic models. In current practice, trained staff manually review digitized images prior to release of these slides to pathologists which are then used to render a diagnosis. Conventional image processing approaches, provide a foundation for detecting artifacts on digital pathology slides. However, current tools do not leverage deep learning, which has the potential to improve detection accuracy and scalability. Despite these advancements, methods for quality assurance in digital pathology remain limited, presenting a gap for innovation. We propose an AI algorithm designed to screen digital pathology slides by analyzing tiles and categorizing them into one of 10 predefined artifact types or as background. This algorithm identifies and localizes artifacts, creating a map that highlights regions of interest. By directing human operators to specific tiles affected by artifacts, the algorithm minimizes the time and effort required to manually review entire slides for quality issues. From internal archives and The Cancer Genome Atlas, 133 whole slide images were selected and 10 artifacts were annotated using an internally developed software ZAPP (Mayo Clinic, Jacksonville, FL). Ablation study of multiple models at different tile sizes and magnification was performed. InceptionResNet was selected. Single artifact models were trained and tested, followed by a limited multiple instance model with artifacts that performed well together (chatter, fold, and pen). From the results of this study we suggest a hybrid design for artifact screening composed of both single artifact binary models as well as multiple instance models to optimize detection of each artifact.
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