Survey on deep learning in multimodal medical imaging for cancer detection
This is an incremental survey paper that synthesizes existing research for researchers in medical imaging and cancer detection.
This survey examines over 150 recent papers on deep learning for multimodal cancer detection, analyzing datasets and solutions to challenges like data annotation, class variance, small lesions, and occlusion, while discussing the advantages and drawbacks of each approach.
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.