A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation. (May 2023)

• Record Type: Journal Article
• Authors: Jiang, Huiyan; Diao, Zhaoshuo; Shi, Tianyu; Zhou, Yang; Wang, Feiyu; Hu, Wenrui; Zhu, Xiaolin; Luo, Shijie; Tong, Guoyu; Yao, Yu-Dong
• Is Part Of: Computers in biology and medicine. Volume 157(2023)
• Publication Date: 2023-05
• Subjects: Medical image -- Deep learning -- Classification -- Detection -- Segmentation; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106726
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Deep learning-based methods have become the dominant methodology in medical image processing with the advancement of deep learning in natural image classification, detection, and segmentation. Deep learning-based approaches have proven to be quite effective in single lesion recognition and segmentation. Multiple-lesion recognition is more difficult than single-lesion recognition due to the little variation between lesions or the too wide range of lesions involved. Several studies have recently explored deep learning-based algorithms to solve the multiple-lesion recognition challenge. This paper includes an in-depth overview and analysis of deep learning-based methods for multiple-lesion recognition developed in recent years, including multiple-lesion recognition in diverse body areas and recognition of whole-body multiple diseases. We discuss the challenges that still persist in the multiple-lesion recognition tasks by critically assessing these efforts. Finally, we outline existing problems and potential future research areas, with the hope that this review will help researchers in developing future approaches that will drive additional advances. Highlights: The first review of the deep learning-based methods for multiple-lesion recognition. The review include multiple-lesion recognition in different body regions. We analyze the problems that still exist in the recognition of multiple-lesion.