BioMed Research International

Machine Learning in Multimodal Medical Imaging


Status
Published

Lead Editor

1Northwestern Polytechnical University, Xi’an, China

2Lomonosov Moscow State University, Moscow, Russia

3University of Sydney, Sydney, Australia

4Lawrence Berkeley National Lab, Berkeley, USA

5Nanjing University of Science and Technology, Nanjing, China


Machine Learning in Multimodal Medical Imaging

Description

Although the last few decades have witnessed the explosive growth in the development and use of noninvasive medical imaging technologies, such as CT, MRI, PET, and SPECT, it was not until recently that multiple imaging modalities began to be incorporated into one single instrument and treated integrally. Multimodal medical imaging provides us with separate yet complementary structure and function information of a patient study in a single imaging session and hence has transformed the way we study living bodies.

Machine learning techniques have been increasingly applied to medical images for developing computer-aided diagnosis and prognosis models. However, machine learning using multimodal medical images is much more challenging than that using single modality images, as multimodal images require sophisticated computing, i.e., reconstruction, restoration, registration, segmentation, and feature extraction, to tackle the variations in image spatial-temporal resolution, as well as the diversity of biophysical-biochemical mechanisms.

We invite investigators to contribute original research articles as well as review articles that will address the machine learning challenges in multimodal medical imaging.

Potential topics include but are not limited to the following:

  • Multimodal medical image reconstruction, restoration, compression, registration, fusion, segmentation, modeling, visualization, and analysis
  • Data mining for multimodal medical images
  • Machine learning models for multimodal medical images
  • Classification, prediction, regression, indexing, and retrieving models for multimodal medical images
  • Deepening learning models for multimodal medical images
  • Computer-aided detection/diagnosis using multimodal medical imaging
  • Patient-centered multimodal medical image interpretation
  • Large-scale evaluation of machine learning techniques applied to multimodal medical images

Articles

  • Special Issue
  • - Volume 2017
  • - Article ID 1278329
  • - Editorial

Machine Learning in Multimodal Medical Imaging

Yong Xia | Zexuan Ji | ... | Weidong Cai
  • Special Issue
  • - Volume 2017
  • - Article ID 1962181
  • - Research Article

A Computer-Aided Analysis Method of SPECT Brain Images for Quantitative Treatment Monitoring: Performance Evaluations and Clinical Applications

Xiujuan Zheng | Wentao Wei | ... | Gang Huang
  • Special Issue
  • - Volume 2017
  • - Article ID 7961494
  • - Research Article

Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images

Jianfeng Zhang | Jiatuo Xu | ... | Ji Cui
  • Special Issue
  • - Volume 2016
  • - Article ID 3510807
  • - Research Article

The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine

Zhen Qi | Li-ping Tu | ... | Zhi-feng Zhang
  • Special Issue
  • - Volume 2016
  • - Article ID 3162649
  • - Research Article

Rapid Retrieval of Lung Nodule CT Images Based on Hashing and Pruning Methods

Ling Pan | Yan Qiang | ... | Lidong Wu
  • Special Issue
  • - Volume 2016
  • - Article ID 3530251
  • - Research Article

Automated Segmentation of Coronary Arteries Based on Statistical Region Growing and Heuristic Decision Method

Yun Tian | Yutong Pan | ... | Wei Wang
  • Special Issue
  • - Volume 2016
  • - Article ID 4674658
  • - Research Article

DTI Image Registration under Probabilistic Fiber Bundles Tractography Learning

Zhe Guo | Yi Wang | ... | Xiuwei Zhang
  • Special Issue
  • - Volume 2016
  • - Article ID 2860643
  • - Research Article

Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging

Shanshan Wang | Jianbo Liu | ... | Dong Liang
  • Special Issue
  • - Volume 2016
  • - Article ID 8052436
  • - Research Article

Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets

Tao Zhou | Huiling Lu | ... | Hongbin Shi
  • Special Issue
  • - Volume 2016
  • - Article ID 6727290
  • - Research Article

Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

Yunjie Chen | Tianming Zhan | ... | Hongyuan Wang
BioMed Research International
 Journal metrics
Acceptance rate31%
Submission to final decision67 days
Acceptance to publication30 days
CiteScore3.600
Impact Factor2.276
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