TY - JOUR
T1 - A review of deep learning-based multiple-lesion recognition from medical images
T2 - classification, detection and segmentation
AU - Jiang, Huiyan
AU - Diao, Zhaoshuo
AU - Shi, Tianyu
AU - Zhou, Yang
AU - Wang, Feiyu
AU - Hu, Wenrui
AU - Zhu, Xiaolin
AU - Luo, Shijie
AU - Tong, Guoyu
AU - Yao, Yu Dong
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/5
Y1 - 2023/5
N2 - 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.
AB - 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.
KW - Classification
KW - Deep learning
KW - Detection
KW - Medical image
KW - Segmentation
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U2 - 10.1016/j.compbiomed.2023.106726
DO - 10.1016/j.compbiomed.2023.106726
M3 - Review article
C2 - 36924732
AN - SCOPUS:85150051660
SN - 0010-4825
VL - 157
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 106726
ER -