  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.
