深度学习用于预测黑色素瘤WSIs的转移
Deep Learning for Predicting Metastasis on Melanoma WSIs
摘要：
北欧是全球黑色素瘤死亡率第二高的国家。于二零二零年，黑色素瘤的死亡率上升至每10万居民1. 9人。黑色素瘤预后是基于病理学家对患者肿瘤的主观视觉分析。这种方法非常耗时，而且专家之间的预后差异很大，严重损害了其可重复性。因此，需要更快和更可重复的方法。机器学习已经为数字病理学铺平了道路，但到目前为止，大多数贡献都集中在定位，分割和诊断上，很少强调病理学。本文提出了一种基于VGG16的卷积神经网络（CNN）方法，用于预测五年内存在转移的黑色素瘤预后。在模型训练和验证中使用的不同放大率水平下，从来自全载玻片图像（WSI）的感兴趣区域提取补丁。结果推断，在20倍放大水平下使用WSI贴片具有最佳性能，F1评分为0.7667，AUC为0.81。
英文摘要：
Northern Europe has the second highest mortality rate of melanoma globally. In 2020, the mortality rate of melanoma rose to 1.9 per 100 000 habitants. Melanoma prognosis is based on a pathologist's subjective visual analysis of the patient's tumor. This methodology is heavily time-consuming, and the prognosis variability among experts is notable, drastically jeopardizing its reproducibility. Thus, the need for faster and more reproducible methods arises. Machine learning has paved its way into digital pathology, but so far, most contributions are on localization, segmentation, and diagnostics, with little emphasis on prognostics. This paper presents a convolutional neural network (CNN) method based on VGG16 to predict melanoma prognosis as the presence of metastasis within five years. Patches are extracted from regions of interest from Whole Slide Images (WSIs) at different magnification levels used in model training and validation. Results infer that utilizing WSI patches at 20x magnification level has the best performance, with an F1 score of 0.7667 and an AUC of 0.81.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
