Efficient Precision Control in Object Detection Models for Enhanced and Reliable Ovarian Follicle Counting
This work addresses the need for reliable and reproducible histopathological analysis in reproductive biology, though it is incremental as it builds on existing precision-recall trade-off methods.
The paper tackles the challenge of controlling prediction precision while maintaining high recall in object detection models for ovarian follicle counting, achieving improved F1-scores through a model-agnostic method that provides probabilistic guarantees on precision.
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.