摘要

Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves laborious free-hand contouring on WSIs. To alleviate the burden of such contouring and obtain benefits from scaling up training with numerous WSIs, we develop a method for training neural networks on entire WSIs using only slide-level diagnoses. Our method leverages the unified memory mechanism to overcome the memory constraint of compute accelerators. Experiments conducted on a data set of 9662 lung cancer WSIs reveal that the proposed method achieves areas under the receiver operating characteristic curve of 0.9594 and 0.9414 for adenocarcinoma and squamous cell carcinoma classification on the testing set, respectively. Furthermore, the method demonstrates higher classification performance than multiple-instance learning as well as strong localization results for small lesions through class activation mapping.
原文英語
文章編號1193
期刊Nature Communications
12
發行號1
DOIs
出版狀態已發佈 - 十二月 2021

ASJC Scopus subject areas

  • 化學 (全部)
  • 生物化學、遺傳與分子生物學 (全部)
  • 物理與天文學 (全部)

指紋

深入研究「An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning」主題。共同形成了獨特的指紋。

引用此