# Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation Source: [Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation]() Collection index: [llms.txt]() Data updated: 2025-10-25T19:11:53.560Z Authors: Jo\\~ao Gustavo Atkinson Amorim, Andr\\'e Vict\\'oria Matias, Allan Cerentini, Luiz Antonio Buschetto Macarini, Alexandre Sherlley Onofre, Fabiana Botelho Onofre, Aldo von Wangenheim Published: 2022-10-19T15:15:32.000Z ## Abstract Cervical cancer is a public health problem, where the treatment has a better chance of success if detected early. The analysis is a manual process which is subject to a human error, so this paper provides a way to analyze argyrophilic nucleolar organizer regions (AgNOR) stained slide using deep learning approaches. Also, this paper compares models for instance and semantic detection approaches. Our results show that the semantic segmentation using U-Net with ResNet-18 or ResNet-34 as the backbone have similar results, and the best model shows an IoU for nucleus, cluster, and satellites of 0.83, 0.92, and 0.99 respectively. For instance segmentation, the Mask R-CNN using ResNet-50 performs better in the visual inspection and has a 0.61 of the IoU metric. We conclude that the instance segmentation and semantic segmentation models can be used in combination to make a cascade model able to select a nucleus and subsequently segment the nucleus and its respective nucleolar organizer regions (NORs). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()