Work

Machine Learning in Multimodal Medical Imaging

Year: 2017

Type: editorial

Abstract: Machine learning techniques have been increasingly applied in the medical imaging field for developing computer-aided diagnosis and prognosis models. Multimodal medical imaging can provide us with separate yet complementary structure and function information of a patient study and hence has transformed the way we study living bodies. Therefore, using machine learning techniques to deal with multimodal medical images is much more challenging due to the diversity of biophysical-biochemical mechanisms. In these years, researchers mainly adapt modern machine learning and pattern recognition techniques such as supervised, unsupervised, semi-supervised, and deep learning to solve multimodal medical imaging related problems.

Source: BioMed Research International

DOI: 10.1155/2017/1278329

Authors: Yong Xia, Zexuan Ji, Andrey Krylov, Hang Chang, Weidong Cai

Institutions: Northwestern Polytechnical University, Nanjing University of Science and Technology, Lomonosov Moscow State University, Lawrence Berkeley National Laboratory, The University of Sydney

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,680