2021年6月30日
Deep Learning Models for Gastric Signet Ring Cell Carcinoma Classification in Whole Slide Images
Technology in Cancer Research & Treatment
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- 巻
- 20
- 号
- 記述言語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1177/15330338211027901
- 出版者・発行元
- SAGE Publications
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 two different test sets (n = 999, n = 455). The best model achieved a ROC-AUC of at least 0.99 on all two test sets, setting a top baseline performance for SRCC WSI classification.
- リンク情報
- ID情報
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- DOI : 10.1177/15330338211027901
- ISSN : 1533-0346
- eISSN : 1533-0338
- DBLP ID : journals/corr/abs-2011-09247
- arXiv ID : arXiv:2011.09247