Conference Papers Year : 2025

Efficient Precision Control in Object Detection Models for Enhanced and Reliable Ovarian Follicle Counting

Abstract

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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Dates and versions

hal-04895227 , version 1 (17-01-2025)

Identifiers

Cite

Vincent Blot, Alexandra Lorenzo de Brionne, Ines Sellami, Olivier Trassard, Isabelle Beau, et al.. Efficient Precision Control in Object Detection Models for Enhanced and Reliable Ovarian Follicle Counting. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, Carole H. Sudre (University College London); Raghav Mehta (Imperial College London); Cheng Ouyang (Oxford University); Chen Qin (Imperial College London); Marianne Rakic (Massachusetts Institute of Technology); William M. Wells (Harvard Medical School), 2024, Marrakesh, Morocco. pp.183-193, ⟨10.1007/978-3-031-73158-7_17⟩. ⟨hal-04895227⟩
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