用AI自动数病理切片碎片，准确率达86%，媲美专家水平。
CountPath: Automating Fragment Counting in Digital Pathology
- 融合YOLOv9与视觉Transformer，实现碎片自动化计数。
- 自动计数准确率86%，与专家间差异范围(82-88%)重合。
- 解决人工计数耗时且主观的问题，适合临床流程集成。
医学图像质量控制是数字病理学的关键环节，确保诊断图像符合标准。其中预分析任务之一是验证载玻片上标本碎片数量，以确保其与大体报告中记录的数量一致，从而保障后续显微检查和诊断的准确性。传统方法依赖人工评估，耗时费力且受主观性影响大。为应对这一挑战，本研究探索使用YOLOv9与视觉变换器模型实现碎片自动计数。结果表明，该系统性能可媲美专家评估，提供可靠高效的替代方案。此外，我们分析了观察者间变异性，发现自动方法准确率为86%，处于专家间变异范围（82%-88%）内，进一步支持其在常规病理流程中集成的潜力。
原文摘要 · Abstract (English)
Quality control of medical images is a critical component of digital pathology, ensuring that diagnostic images meet required standards. A pre-analytical task within this process is the verification of the number of specimen fragments, a process that ensures that the number of fragments on a slide matches the number documented in the macroscopic report. This step is important to ensure that the slides contain the appropriate diagnostic material from the grossing process, thereby guaranteeing the accuracy of subsequent microscopic examination and diagnosis. Traditionally, this assessment is performed manually, requiring significant time and effort while being subject to significant variability due to its subjective nature. To address these challenges, this study explores an automated approach to fragment counting using the YOLOv9 and Vision Transformer models. Our results demonstrate that the automated system achieves a level of performance comparable to expert assessments, offering a reliable and efficient alternative to manual counting. Additionally, we present findings on interobserver variability, showing that the automated approach achieves an accuracy of 86%, which falls within the range of variation observed among experts (82-88%), further supporting its potential for integration into routine pathology workflows.
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