TY - JOUR
T1 - Survey on deep learning in multimodal medical imaging for cancer detection
AU - Tian, Yan
AU - Xu, Zhaocheng
AU - Ma, Yujun
AU - Ding, Weiping
AU - Wang, Ruili
AU - Gao, Zhihong
AU - Cheng, Guohua
AU - He, Linyang
AU - Zhao, Xuran
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
PY - 2023
Y1 - 2023
N2 - The task of multimodal cancer detection is to determine the locations and categories of lesions by using different imaging techniques, which is one of the key research methods for cancer diagnosis. Recently, deep learning-based object detection has made significant developments due to its strength in semantic feature extraction and nonlinear function fitting. However, multimodal cancer detection remains challenging due to morphological differences in lesions, interpatient variability, difficulty in annotation, and imaging artifacts. In this survey, we mainly investigate over 150 papers in recent years with respect to multimodal cancer detection using deep learning, with a focus on datasets and solutions to various challenges such as data annotation, variance between classes, small-scale lesions, and occlusion. We also provide an overview of the advantages and drawbacks of each approach. Finally, we discuss the current scope of work and provide directions for the future development of multimodal cancer detection.
AB - The task of multimodal cancer detection is to determine the locations and categories of lesions by using different imaging techniques, which is one of the key research methods for cancer diagnosis. Recently, deep learning-based object detection has made significant developments due to its strength in semantic feature extraction and nonlinear function fitting. However, multimodal cancer detection remains challenging due to morphological differences in lesions, interpatient variability, difficulty in annotation, and imaging artifacts. In this survey, we mainly investigate over 150 papers in recent years with respect to multimodal cancer detection using deep learning, with a focus on datasets and solutions to various challenges such as data annotation, variance between classes, small-scale lesions, and occlusion. We also provide an overview of the advantages and drawbacks of each approach. Finally, we discuss the current scope of work and provide directions for the future development of multimodal cancer detection.
KW - Cancer detection
KW - Computer vision
KW - Convolutional neural network
KW - Medical image analysis
UR - http://www.scopus.com/inward/record.url?scp=85177869103&partnerID=8YFLogxK
U2 - 10.1007/s00521-023-09214-4
DO - 10.1007/s00521-023-09214-4
M3 - Article
AN - SCOPUS:85177869103
SN - 0941-0643
JO - Neural Computing and Applications
JF - Neural Computing and Applications
ER -