用于语义分割的深度监督UNet，以辅助基底细胞癌（BCC）的皮肤病理学评估
Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of Basal Cell Carcinoma (BCC)
摘要：
准确和快速评估切缘是皮肤病理学家临床常规的重要组成部分。在这项工作中，我们成功地开发了一种深度学习方法，通过标记在全载玻片图像（WSI）中具有高概率表现出病理特征的关键区域来帮助病理学家。我们专注于检测基底细胞癌（BCC），通过语义分割使用几个模型的基础上UNet架构。该研究包括650个WSI，共3443个组织切片。两名临床皮肤病理学家对数据进行注释，在100 WSI上标记肿瘤组织的确切位置。其余的数据，与地面实况部分明智的标签，用于进一步验证和测试模型。我们分析了UNet网络第一部分的两种不同编码器和两种额外的训练策略：a）深度监督，b）解码器输出的线性组合，并获得了关于网络解码器在每种情况下的作用的一些解释。最好的模型在测试集上达到96%以上的准确性，灵敏度和特异性。
英文摘要：
Accurate and fast assessment of resection margins is an essential part of a dermatopathologist's clinical routine. In this work, we successfully develop a deep learning method to assist the pathologists by marking critical regions that have a high probability of exhibiting pathological features in Whole Slide Images (WSI). We focus on detecting Basal Cell Carcinoma (BCC) through semantic segmentation using several models based on the UNet architecture. The study includes 650 WSI with 3443 tissue sections in total. Two clinical dermatopathologists annotated the data, marking tumor tissues' exact location on 100 WSI. The rest of the data, with ground-truth section-wise labels, is used to further validate and test the models. We analyze two different encoders for the first part of the UNet network and two additional training strategies: a) deep supervision, b) linear combination of decoder outputs, and obtain some interpretations about what the network's decoder does in each case. The best model achieves over 96%, accuracy, sensitivity, and specificity on the test set.
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