BioMed Research International

Representation Learning in Radiology


Publishing date
01 Mar 2021
Status
Closed
Submission deadline
13 Nov 2020

Lead Editor

1University of Texas Southwestern Medical Center, Dallas, USA

2University of Central Missouri, Warrensburg, USA

3Shenzhen Institutes of Advanced Technology - Chinese Academy of Sciences, Shenzhen, China

This issue is now closed for submissions.
More articles will be published in the near future.

Representation Learning in Radiology

This issue is now closed for submissions.
More articles will be published in the near future.

Description

The development, deployment, and evolution of representation learning has been used in radiology for intelligent diagnosis, treatment outcome prediction, and biomarker discovery. Representation learning explores how to transform data into quantitative features and to facilitate automatic data analysis.

At present, radiomics and deep learning are still in development, and challenges still exist – e.g., how to automatically extract features with clinical meanings, how to train a deep network with a small number of data samples, how to fuse multi-source information, and how to design representation learning with high interpretability.

This Special Issue calls for submissions of original research and review articles to address these challenges and to highlight the recent progress of representation learning in radiology and related fields. We are particularly interested in articles that could deepen our understanding of representation learning in clinical applications with high interpretability. In addition, articles to uncover clinical and technical challenges are also welcomed.

Potential topics include but are not limited to the following:

  • Biomedical data representation and automatic data analysis
  • Recent progress in radiomics, delta radiomics and deep learning
  • Advanced technologies in multi-source information fusion
  • Feature engineering in computer-aided detection and diagnosis
  • Representation learning for disease diagnosis and biomarker discovery
  • Data representation in the prediction of treatment outcome
  • Integrated studies of representation learning and clinical applications

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 8811056
  • - Research Article

Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors

Derun Pan | Renyi Liu | ... | Weiguo Chen
  • Special Issue
  • - Volume 2021
  • - Article ID 6685943
  • - Research Article

A Sparse Volume Reconstruction Method for Fetal Brain MRI Using Adaptive Kernel Regression

Qian Ni | Yi Zhang | ... | Ling Li
  • Special Issue
  • - Volume 2021
  • - Article ID 8840835
  • - Research Article

An Interpretable Model-Based Prediction of Severity and Crucial Factors in Patients with COVID-19

Bowen Zheng | Yong Cai | ... | Yi Guo
  • Special Issue
  • - Volume 2021
  • - Article ID 6679603
  • - Research Article

Automated Segmentation Method for Low Field 3D Stomach MRI Using Transferred Learning Image Enhancement Network

Luguang Huang | Mengbin Li | ... | Kun Jiang
  • Special Issue
  • - Volume 2021
  • - Article ID 6668510
  • - Research Article

To Explore MR Imaging Radiomics for the Differentiation of Orbital Lymphoma and IgG4-Related Ophthalmic Disease

Ye Yuan | Guangyu Chu | ... | Qinghe Han
  • Special Issue
  • - Volume 2021
  • - Article ID 6621894
  • - Research Article

Development and Validation of a Radiomics Nomogram for Prognosis Prediction of Patients with Acute Paraquat Poisoning: A Retrospective Cohort Study

Shan Lu | Duo Gao | ... | Zuojun Geng
  • Special Issue
  • - Volume 2021
  • - Article ID 1235314
  • - Research Article

Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach

Mengqiu Cao | Shiteng Suo | ... | Yan Zhou
  • Special Issue
  • - Volume 2021
  • - Article ID 6683931
  • - Research Article

Automated Classification and Segmentation in Colorectal Images Based on Self-Paced Transfer Network

Yao Yao | Shuiping Gou | ... | Shuixiang He
  • Special Issue
  • - Volume 2021
  • - Article ID 2043830
  • - Research Article

DVH Prediction for VMAT in NPC with GRU-RNN: An Improved Method by Considering Biological Effects

Yongdong Zhuang | Yaoqin Xie | ... | Yuenan Wang
  • Special Issue
  • - Volume 2020
  • - Article ID 5042356
  • - Research Article

Differential Diagnosis of Solitary Fibrous Tumor/Hemangiopericytoma and Angiomatous Meningioma Using Three-Dimensional Magnetic Resonance Imaging Texture Feature Model

Junyi Dong | Meimei Yu | ... | Lizhi Xie
BioMed Research International
 Journal metrics
Acceptance rate31%
Submission to final decision67 days
Acceptance to publication30 days
CiteScore4.100
Journal Citation Indicator0.610
Impact Factor3.411
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.