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Campanella, G., Hanna, M.G., Geneslaw, L., Miraflor, A., Silva, V.W.K., Busam, K.J., Brogi, E., Reuter, V.E., Klimstra, D.S. and Fuchs, T.J. (2019) Clinical-Grade Computational Pathology Using Weakly Supervised Deep Learning on Whole Slide Images. Nature Medicine, 25, 1301-1309.
https://doi.org/10.1038/s41591-019-0508-1

has been cited by the following article:

• TITLE: Detection and Classification of Lung Cancer Cells Using Swin Transformer

  AUTHORS: Yuru Chen, Jing Feng, Juan Liu, Baochuan Pang, Dehua Cao, Cheng Li

  KEYWORDS: Lung Cancer, Classification, Swin Transformer

  JOURNAL NAME: Journal of Cancer Therapy, Vol.13 No.7, July 21, 2022

  ABSTRACT: Lung cancer is one of the greatest threats to human health. It is a very effective way to detect lung cancer by pathological pictures of lung cancer cells. Therefore, improving the accuracy and stability of diagnosis is very important. In this study, we develop an automatic detection scheme for lung cancer cells based on convolutional neural networks and Swin Transformer. Microscopic images of patients’ lung cells are first segmented using a Mask R-CNN-based network, resulting in a separate image for each cell. Part of the background information is preserved by Gaussian blurring of surrounding cells, while the target cells are highlighted. The classification model based on Swin Transformer not only reduces the computation but also achieves better results than the classical CNN model, ResNet50. The final results show that the accuracy of the method proposed in this paper reaches 96.16%. Therefore, this method is helpful for the detection and classification of lung cancer cells.