目标检测模型中的高效精度控制用于增强和可靠地卵巢卵泡计数
Efficient Precision Control in Object Detection Models for Enhanced and Reliable Ovarian Follicle Counting
AI总结：
本研究提出一种模型无关的上下文决策阈值选择方法，结合多重检验程序控制精度，提升卵巢卵泡计数模型的F1分数，无需重新训练即可增强性能。
摘要：
图像分析是描述卵泡发生详细机制的关键工具，例如评估卵巢储备中小鼠原始卵巢卵泡（PMF）的数量。高分辨率虚拟玻片扫描仪的发展为量化、增强稳健性并加速组织病理学流程提供了可能性。机器学习面临的一个主要挑战是在实现高召回率的同时控制预测的精度，以提供可重复性。我们采用一种多重检验程序，该程序以优于标准方式解决精度-召回率权衡问题，并为精度提供概率性保证。此外，我们通过利用上下文生物学信息或使用辅助模型来选择决策阈值，显著提升了模型的整体性能（增加了F1分数）。由于该程序与模型无关，这种上下文选择方法为开发一种无需重新训练即可提升任何模型性能的策略铺平了道路。
英文摘要：
Image analysis is a key tool for describing the detailed mechanisms of folliculogenesis, such as evaluating the quantity of mouse Primordial ovarian Follicles (PMF) in the ovarian reserve. The development of high-resolution virtual slide scanners offers the possibility of quantifying, robustifying and accelerating the histopathological procedure. A major challenge for machine learning is to control the precision of predictions while enabling a high recall, in order to provide reproducibility. We use a multiple testing procedure that gives an overperforming way to solve the standard Precision-Recall trade-off that gives probabilistic guarantees on the precision. In addition, we significantly improve the overall performance of the models (increase of F1-score) by selecting the decision threshold using contextual biological information or using an auxiliary model. As it is model-agnostic, this contextual selection procedure paves the way to the development of a strategy that can improve the performance of any model without the need of retraining it.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
