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 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
  • Special Issue
  • - Volume 2020
  • - Article ID 8864756
  • - Research Article

Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis

Yun Guan | Peng Wang | ... | Yanfeng Meng
  • Special Issue
  • - Volume 2020
  • - Article ID 5193707
  • - Research Article

Comparison of Supervised and Unsupervised Deep Learning Methods for Medical Image Synthesis between Computed Tomography and Magnetic Resonance Images

Yafen Li | Wen Li | ... | Yaoqin Xie
  • Special Issue
  • - Volume 2020
  • - Article ID 5491963
  • - Research Article

Application of BERT to Enable Gene Classification Based on Clinical Evidence

Yuhan Su | Hongxin Xiang | ... | Na Zhao
  • Special Issue
  • - Volume 2020
  • - Article ID 6287545
  • - Research Article

Machine Learning-Based Differentiation of Nontuberculous Mycobacteria Lung Disease and Pulmonary Tuberculosis Using CT Images

Zhiheng Xing | Wenlong Ding | ... | Zhaoxiang Ye
  • Special Issue
  • - Volume 2020
  • - Article ID 9258649
  • - Research Article

Automatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis

Xin Chen | Min Zeng | ... | Xinqing Jiang
  • Special Issue
  • - Volume 2020
  • - Article ID 6863231
  • - Research Article

MAGE-Targeted Gold Nanoparticles for Ultrasound Imaging-Guided Phototherapy in Melanoma

Xuelin Li | Shigen Zhong | ... | Zhigang Wang
  • Special Issue
  • - Volume 2020
  • - Article ID 7103647
  • - Research Article

A CT-Based Radiomics Approach for the Differential Diagnosis of Sarcomatoid and Clear Cell Renal Cell Carcinoma

Xiaoli Meng | Jun Shu | ... | Ruwu Yang
  • Special Issue
  • - Volume 2020
  • - Article ID 5615371
  • - Research Article

To Align Multimodal Lumbar Spine Images via Bending Energy Constrained Normalized Mutual Information

Shibin Wu | Pin He | ... | Yaoqin Xie
BioMed Research International
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.