Survey on deep learning in multimodal medical imaging for cancer detection

Yan Tian, Zhaocheng Xu, Yujun Ma, Weiping Ding, Ruili Wang, Zhihong Gao, Guohua Cheng, Linyang He, Xuran Zhao

Research output: Journal PublicationArticlepeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
JournalNeural Computing and Applications
DOIs
Publication statusAccepted/In press - 2023
Externally publishedYes

Keywords

  • Cancer detection
  • Computer vision
  • Convolutional neural network
  • Medical image analysis

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Survey on deep learning in multimodal medical imaging for cancer detection'. Together they form a unique fingerprint.

Cite this