PDF(Pubmed)
Abstract：
Background: Deep learning models for patch classification in whole-slide images (WSIs) have shown promise in assisting follicular lymphoma grading. However, these models often require pathologists to identify centroblasts and manually provide refined labels for model optimization. Objective: To address this limitation, we propose PseudoCell, an object detection framework for automated centroblast detection in WSI, eliminating the need for extensive pathologist\'s refined labels. Methods: PseudoCell leverages a combination of pathologist-provided centroblast labels and pseudo-negative labels generated from undersampled false-positive predictions based on cell morphology features. This approach reduces the reliance on time-consuming manual annotations. Results: Our framework significantly reduces the workload for pathologists by accurately identifying and narrowing down areas of interest containing centroblasts. Depending on the confidence threshold, PseudoCell can eliminate 58.18-99.35% of irrelevant tissue areas on WSI, streamlining the diagnostic process. Conclusion: This study presents PseudoCell as a practical and efficient prescreening method for centroblast detection, eliminating the need for refined labels from pathologists. The discussion section provides detailed guidance for implementing PseudoCell in clinical practice.
摘要：
背景：全幻灯片图像（WSI）中用于斑块分类的深度学习模型在辅助滤泡性淋巴瘤分级方面显示出了希望。然而,这些模型通常需要病理学家来识别成中心细胞并手动提供精确的标签以进行模型优化.目标：为了解决这一限制，我们提出伪细胞，WSI中用于自动着丝粒检测的对象检测框架，消除了对广泛病理学家的精致标签的需要。方法：PseudoCell利用病理学家提供的成中心细胞标签和基于细胞形态特征的低采样假阳性预测产生的伪阴性标签的组合。这种方法减少了对耗时的手动注释的依赖。结果：我们的框架通过准确识别和缩小含有成中心细胞的感兴趣区域，显著减少了病理学家的工作量。根据置信度阈值，PseudoCell可以消除WSI上58.18-99.35％的无关组织区域，简化诊断过程。结论：本研究提出了PseudoCell作为成中心细胞检测的一种实用有效的预筛选方法，消除了病理学家对精细标签的需求。讨论部分提供了在临床实践中实施PseudoCell的详细指导。
