文章：

保持病理学家参与循环与自适应F1分数阈值方法在犬血管周壁肿瘤有丝分裂检测中的应用

Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours

原文发布日期：2 February 2024

DOI: 10.3390/cancers16030644

类型: Article

开放获取: 是

英文摘要：

Performing a mitosis count (MC) is the diagnostic task of histologically grading canine Soft Tissue Sarcoma (cSTS). However, mitosis count is subject to inter- and intra-observer variability. Deep learning models can offer a standardisation in the process of MC used to histologically grade canine Soft Tissue Sarcomas. Subsequently, the focus of this study was mitosis detection in canine Perivascular Wall Tumours (cPWTs). Generating mitosis annotations is a long and arduous process open to inter-observer variability. Therefore, by keeping pathologists in the loop, a two-step annotation process was performed where a pre-trained Faster R-CNN model was trained on initial annotations provided by veterinary pathologists. The pathologists reviewed the output false positive mitosis candidates and determined whether these were overlooked candidates, thus updating the dataset. Faster R-CNN was then trained on this updated dataset. An optimal decision threshold was applied to maximise the F1-score predetermined using the validation set and produced our best F1-score of 0.75, which is competitive with the state of the art in the canine mitosis domain.

摘要翻译： 

进行有丝分裂计数（MC）是组织学分级犬软组织肉瘤（cSTS）的诊断任务。然而，有丝分裂计数存在观察者间和观察者内的变异性。深度学习模型可为用于犬软组织肉瘤组织学分级的MC过程提供标准化方法。因此，本研究聚焦于犬血管周壁肿瘤（cPWTs）的有丝分裂检测。生成有丝分裂标注是一个漫长且艰巨的过程，同样存在观察者间变异性。为此，本研究采用病理学家参与的两步标注流程：首先基于兽医病理学家提供的初始标注训练预训练的Faster R-CNN模型；随后病理学家审阅模型输出的假阳性有丝分裂候选区域，判断是否为被遗漏的阳性样本，从而更新数据集。基于更新后的数据集重新训练Faster R-CNN模型，通过应用经验证集预定的最优决策阈值以最大化F1分数，最终获得0.75的最佳F1分数，该结果在犬类有丝分裂检测领域达到先进水平。

原文链接：

Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours