Efficient Precision Control in Object Detection Models for Enhanced and Reliable Ovarian Follicle Counting
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916581452283904 |
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| author | Blot, Vincent de Brionne, Alexandra Lorenzo Sellami, Ines Trassard, Olivier Beau, Isabelle Sonigo, Charlotte Brunel, Nicolas J-B. |
| author_facet | Blot, Vincent de Brionne, Alexandra Lorenzo Sellami, Ines Trassard, Olivier Beau, Isabelle Sonigo, Charlotte Brunel, Nicolas J-B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14036 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Precision Control in Object Detection Models for Enhanced and Reliable Ovarian Follicle Counting Blot, Vincent de Brionne, Alexandra Lorenzo Sellami, Ines Trassard, Olivier Beau, Isabelle Sonigo, Charlotte Brunel, Nicolas J-B. Machine Learning 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. |
| title | Efficient Precision Control in Object Detection Models for Enhanced and Reliable Ovarian Follicle Counting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.14036 |