Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation
It addresses the need for automated, error-free analysis in cervical cancer screening, though it is incremental as it applies existing methods to a specific medical imaging task.
This paper tackles the problem of automating the analysis of AgNOR-stained cytology samples for early cervical cancer detection by comparing deep learning approaches, achieving IoU scores up to 0.99 for semantic segmentation and 0.61 for instance segmentation.
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).