Cited by CrossRef (11)

  1. Yuxue Zhao, Bo Hu, Ying Wang, Xiaomeng Yin, Yuanyuan Jiang, Xiuli Zhu. Identification of gastric cancer with convolutional neural networks: a systematic review. Multimed Tools Appl 2022;81:11717
  2. Bangkang Fu, Mudan Zhang, Junjie He, Ying Cao, Yuchen Guo, Rongpin Wang. StoHisNet: A hybrid multi-classification model with CNN and Transformer for gastric pathology images. Computer Methods and Programs in Biomedicine 2022;221:106924
  3. JaeYen Song, Soyoung Im, Sung Hak Lee, Hyun-Jong Jang. Deep Learning-Based Classification of Uterine Cervical and Endometrial Cancer Subtypes from Whole-Slide Histopathology Images. Diagnostics 2022;12:2623
  4. Hyun-Jong Jang, Ahwon Lee, Jun Kang, In Hye Song, Sung Hak Lee. Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach. WJG 2021;27:7687
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  7. Hyun-Jong Jang, In-Hye Song, Sung-Hak Lee. Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images. Cancers 2021;13:3811
  8. Sung Hak Lee, In Hye Song, Hyun‐Jong Jang. Feasibility of deep learning‐based fully automated classification of microsatellite instability in tissue slides of colorectal cancer. Int. J. Cancer 2021;149:728
  9. Shiliang Ai, Chen Li, Xiaoyan Li, Tao Jiang, Marcin Grzegorzek, Changhao Sun, Md Mamunur Rahaman, Jinghua Zhang, Yudong Yao, Hong Li, Yong Xia. A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development. BioMed Research International 2021;2021:1
  10. Hyun-Jong Jang, Ahwon Lee, J Kang, In Hye Song, Sung Hak Lee. Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning. WJG 2020;26:6207
  11. Hyun-Jong Jang, In Hye Song, Sung Hak Lee. Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers. Applied Sciences 2021;11:808