论文标题
全幻灯片图像中的胃网环细胞癌的深度学习模型
Deep learning models for gastric signet ring cell carcinoma classification in whole slide images
论文作者
论文摘要
胃的标志性环细胞癌（SRCC）是一种罕见的癌症类型，发病率缓慢上升。病理学家主要由于其细胞形态和弥漫性侵袭方式而被病理学家检测到更困难，并且在晚期检测到预后不良。可以帮助病理学家检测SRCC的计算病理学工具将带来巨大的好处。在本文中，我们使用转移学习，完全监督的学习和弱监督的学习训练了深度学习模型，以使用1,765 WSIS的培训集预测整个幻灯片图像（WSIS）中的SRCC。我们在四个不同的测试集上评估了每个大约500张图像的模型。最佳模型在所有四个测试集上达到了至少0.99的接收器操作员曲线（ROC）区域，为SRCC WSI分类设置了顶部基线性能。
Signet ring cell carcinoma (SRCC) of the stomach is a rare type of cancer with a slowly rising incidence. It tends to be more difficult to detect by pathologists mainly due to its cellular morphology and diffuse invasion manner, and it has poor prognosis when detected at an advanced stage. Computational pathology tools that can assist pathologists in detecting SRCC would be of a massive benefit. In this paper, we trained deep learning models using transfer learning, fully-supervised learning, and weakly-supervised learning to predict SRCC in Whole Slide Images (WSIs) using a training set of 1,765 WSIs. We evaluated the models on four different test sets of about 500 images each. The best model achieved a Receiver Operator Curve (ROC) area under the curve (AUC) of at least 0.99 on all four test sets, setting a top baseline performance for SRCC WSI classification.
