纤维腺瘤
卷积神经网络
人工智能
联营
活检
核心活检
模式识别(心理学)
计算机科学
病理
医学
内科学
乳腺癌
癌症
作者
Chee Leong Cheng,Nur Diyana Md Nasir,Gary Jian Zhe Ng,Kenny Wei Jie Chua,Yier Li,Joshua Stephen Rodrigues,Aye Aye Thike,Seow Ye Heng,Valerie Cui Yun Koh,Jimmy Lim,Venice Jing Ning Hiew,Ruo-Yu Shi,Benjamin Y. Tan,Timothy Kwang Yong Tay,Sudha Ravi,Kim Hock Ng,Kevin Seng Loong Oh,Puay Hoon Tan
标识
DOI:10.1038/s41374-021-00689-0
摘要
Breast fibroepithelial lesions (FEL) are biphasic tumors which consist of benign fibroadenomas (FAs) and the rarer phyllodes tumors (PTs). FAs and PTs have overlapping features, but have different clinical management, which makes correct core biopsy diagnosis important. This study used whole-slide images (WSIs) of 187 FA and 100 PT core biopsies, to investigate the potential role of artificial intelligence (AI) in FEL diagnosis. A total of 9228 FA patches and 6443 PT patches was generated from WSIs of the training subset, with each patch being 224 × 224 pixel in size. Our model employed a two-stage architecture comprising a convolutional neural network (CNN) component for feature extraction from the patches, and a recurrent neural network (RNN) component for whole-slide classification using activation values from the global average pooling layer in the CNN model. It achieved an overall slide-level accuracy of 87.5%, with accuracies of 80% and 95% for FA and PT slides respectively. This affirms the potential role of AI in diagnostic discrimination between FA and PT on core biopsies which may be further refined for use in routine practice.
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