通过上下文信息优化阈值，提升卵巢卵泡计数的精度与可靠性。
Efficient Precision Control in Object Detection Models for Enhanced and Reliable Ovarian Follicle Counting
- 利用多重检验方法控制预测精度，缓解精确率-召回率权衡问题。
- 结合生物背景信息选择决策阈值，使模型F1分数显著提升。
- 方法不依赖特定模型，可通用提升各类检测模型性能。
图像分析是揭示卵泡发生机制的关键工具，例如评估小鼠原始卵泡（PMF）数量以评估卵巢储备。高分辨率虚拟切片扫描仪的发展使得组织病理学分析的量化、鲁棒性和速度得到提升。机器学习面临的核心挑战在于，在保持高召回率的同时控制预测精度，以确保结果可重复。本文采用多重检验程序，提供了一种优于传统方法的精度控制策略，并为精确率提供了概率保障。此外，通过结合上下文生物学信息或使用辅助模型选择决策阈值，显著提升了整体模型性能（F1分数提高）。该方法具有模型无关性，无需重新训练即可改善任意模型的表现，为通用性能增强策略开辟了道路。
原文摘要 · Abstract (English)
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.
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