## 5 Results and Discussion
### Post-Processing Optimizations  
The results reported in the previous sections allowed us to select the best combination of optimization steps during model training, which maximized the cervical lesion detection performance at the patch-level on the validation set. Using the best-performing model achieved after training, the class-wise optimization of the prediction score threshold was further explored to improve the model's image-level performance on the test set. The confusion matrices obtained for the best-performing model before and after the score threshold optimization are depicted in Figure 9.  
To support the critical analysis of these results, additional performance metrics were extracted from both confusion matrices, namely accuracy, specificity, F1 score, and Youden's index (see Table 5). Due to the class imbalance of the dataset, the F1 score was used as the primary criterion for selecting the optimal score threshold for each class. This led to F1 score improvements in all classes, except for LSIL, with still no TPs detected. In particular, the F1 score of ASC-US class increased 47% due to a favorable decrease of 887 FPs, but with the compromise of losing 22 TPs. On the other hand, the F1 score of normal squamous  
Figure 8: [EMAIL_ADDRESS] IoU (**a**) and AR@10 (**b**) results for the test set for the proposed nucleus-based and the previously proposed region-based approaches [6]. Figure 9: Confusion matrices for the best-performing model before (**a**) and after (**b**) the score threshold optimization. nuclei (Sqm. Normal) increased 5% due to a favorable increase of 1376 TPs, but with the shortcoming of detecting 871 additional FPs. However, similar behavior was verified in the ASC-H and HSIL classes, with just residual increases in both TPs and FPs. From the clinical point of view, the described trade-offs between TPs and FPs seem to benefit the usage of the model after optimization for screening purposes. First, because Sqm. Normal detections are not cervical lesions, they do not represent priority findings that need to be mandatorily reviewed by the screening cytopathologists. For this reason, the significant increase of FPs for the Sqm. After optimization, the normal class on the model does not necessarily cause overhead in the clinical flow. And second, the demarcated decrease in ASC-US FPs on the model after optimization could actually have a relevant impact on its suitability to support clinical decisions because these are abnormal findings that need to be reviewed by cytopathologists, forcing them to constantly review large numbers of FPs, which can lead to an unfeasible overhead in the screening process. Thus, the performance improvements verified for the lesion classes demonstrated the benefits of the data and model-centric optimizations applied during training and post-processing steps, resulting in a more robust lesion detection system with potential to streamline the clinical workflow of cervical screening processes. Some illustrative examples of correctly and incorrectly classified images on the test set are shown in Figure 10.  
In summary, this work contributes to the advancement of mobile-based cervical cytology screening by demonstrating the potential of using nucleus-based approaches that can represent a complementary crucial tool to increase the wide spread of cervical cytology screening and the early and accurate diagnosis of cervical dysplasias. By expanding the coverage of screening programs in underserved areas, these tools can contribute to decreasing the extension of excisional treatments, which are responsible for adverse effects like increased risk of preterm delivery, lower birth weight, or preterm premature rupture of membrane before 37 weeks of pregnancy [40]. These tools can also be coupled to the analysis of risk factors like HPV persistence and the positivity of surgical resection to leverage a more widespread early detection of recurrence after surgical treatment [41].