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

• URL: http://arxiv.org/abs/2501.14036v1
• Date: Thu, 23 Jan 2025 19:04:47 GMT
• Title: Efficient Precision Control in Object Detection Models for Enhanced and Reliable Ovarian Follicle Counting
• Authors: Vincent Blot, Alexandra Lorenzo de Brionne, Ines Sellami, Olivier Trassard, Isabelle Beau, Charlotte Sonigo, Nicolas J-B. Brunel,
• Abstract summary: A major challenge for machine learning is to control the precision of predictions while enabling a high recall. We use a multiple testing procedure that gives an overperforming way to solve the standard Precision-Recall trade-off. 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.
• Score: 37.9434503914985
• License:
• 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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