A multi-task deep learning framework for perineural invasion recognition in gastric cancer whole slide images. (January 2023)

• Record Type: Journal Article
• Authors: Hu, Ziwei; Deng, Yanglin; Lan, Junlin; Wang, Tao; Han, Zixin; Huang, Yuxiu; Zhang, Hejun; Wang, Jianchao; Cheng, Musheng; Jiang, Haiyan; Lee, Ren-Guey; Du, Ming; Tong, Tong; Gao, Qinquan; Chen, Gang
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 2
• Publication Date: 2023-01
• Subjects: Perineural infiltration detection -- Gastric cancer segmentation -- Whole slide images -- Deep learning -- Transformer; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104261
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Perineural invasion (PNI) is the process of neoplastic invasion of the nerves and is a prognostic factor in gastric cancer. However, such examination is a labor-intensive task in high-resolution digital pathological images. To alleviate this problem, we propose a multi-task deep learning-based framework to highlight diagnostically significant PNI regions in whole slide images (WSIs) of human gastric cancer tissue sections. The proposed framework includes a gastric cancer segmentation model, neural detection model, and PNI decision-making module, which realizes the segmentation of the gastric cancer region while completing the task of identifying PNI. Adequate comparative experiments were performed on our own gastric cancer PNI dataset called GC-PNI. Experiments have shown that our proposed model can effectively diagnose PNI with a high sensitivity of 0.972 and a specificity of 0.933, illustrating the potential of this practical application. Highlights: Proposing a deep learning framework for perineural invasion recognition in whole slide images of gastric cancer. Designing a PNI decision-making module for recognizing perineural invasion. The proposed framework achieved better performance. Creating GC-PNI dataset for perineural invasion and gastric cancer.