Azad, R., Asadi-Aghbolaghi, M., Fathy, M. and Escalera, S. (2019) Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions. Proceedings of the IEEE/ CVF International Conference on Computer Vision Workshops, Seoul, 27-28 October 2019, 406-415.
https://doi.org/10.1109/ICCVW.2019.00052

has been cited by the following article:

• TITLE: Two-Stage Segmentation of Lung Cancer Metastasis Lesions by Fusion of Multi-Resolution Features

  AUTHORS: Jingwen Zhao, Xinyu Wang, Yunlang She, Shuohong Wang

  KEYWORDS: Transfer Learning, Pathological Image, ACR-UNet, Deep Learning, Cancer Metastasis

  JOURNAL NAME: Health, Vol.15 No.5, May 26, 2023

  ABSTRACT: The deep learning method automatically extracts advanced features from a large amount of data, avoiding cumbersome manual feature screening, and using digital pathology and artificial intelligence technology to build a computer-aided diagnosis system to help pathologists quickly make objective and reliable diagnoses and improve work efficiency. Because pathological images are limited by factors such as sample size, manual labeling expertise, and complexity, artificial intelligence algorithms have not been extensively and in-depth researched on pathological images of lung cancer metastasis. Therefore, this paper proposes a lung cancer metastasis segmentation method based on pathological images, to further improve the computer-aided diagnosis method of lung cancer.