论文标题
对Agnor染色细胞学样本的深度学习方法的比较分析解释
Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation
论文作者
论文摘要
宫颈癌是一个公共卫生问题，如果早期发现该治疗的可能性更大。该分析是一种手动过程，遭受人为误差的影响，因此本文提供了一种分析使用深度学习方法染色幻灯片的杂货核仁组织者区域（AGNOR）的方法。此外，本文比较了模型和语义检测方法。我们的结果表明，使用u-NET与Resnet-18或Resnet-34作为骨架的语义分割具有相似的结果，最佳模型分别显示了核，簇和卫星的IOU，分别为0.83、0.92和0.99。例如分割，使用Resnet-50的掩模R-CNN在视觉检查中的性能更好，并且具有0.61的IOU度量。我们得出的结论是，可以将实例分割和语义分割模型组合使用，以使级联模型能够选择核，然后将细胞核及其各自的核仁组织者区域（NORS）分割。
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).
