Special Issue "Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph"

Submission Deadline: 31 October 2021
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Guest Editors
Dr. Shuihua Wang, University of Leicester, United Kingdom
Dr. Zheng Zhang, Harbin Institute of Technology, China
Dr. Yuankai Huo, University of Vanderbilt, United States


The chest radiograph has been one of the most frequently performed radiological investigation tools. In clinical medicine, the chest radiograph can provide technical basis and scientific instruction to recognize a series of thoracic diseases (such as Atelectasis, Nodule, and Pneumonia, etc.). Importantly, it is of paramount importance for clinical screening, diagnosis, treatment planning, and efficacy evaluation. However, it remains challenging for automated chest radiograph diagnosis and interpretation at the level of an experienced radiologists. In recent years, many studies on biomedical image processing have advanced rapidly with the development of artificial intelligence especially deep learning techniques and algorithms. How to build an efficient and accurate deep learning model for automatic chest radiograph processing is an important scientific problem that needs to be solved.


To address the unique problems in diagnosis of chest radiological images, various techniques need to be developed to advance such research fields. This special issue intends to demonstrate the new development and application of automatic chest radiograph processing, and thereby promote the research and development of chest radiology imaging in biomedical intelligence. The goal is to provide a platform for researchers to disseminate their recent advances and views of computer-assisted imaging processing and machine learning applications on diagnosis of chest Radiograph by publishing high-quality research papers in this interdisciplinary field.


Potential topics include, but are not limited to:

• Diagnosis of chest radiological image based on machine learning;

• Supervised or semi-supervised learning for lung, nodule, and lesion segmentation;

• Transfer learning for chest radiological image analysis;

• Multi-task learning, i.e., joint image segmentation and image-based diagnosis for chest radiological image;

• Early prediction of Tuberculosis or Nodule progress, i.e., from mild to severe or critical based on machine learning;

• Noisy label processing, i.e., diagnosis of chest radiological image with noisy (or incorrect) labels in some training samples;

• The publication of large-scale chest radiological image databases;

• Automated chest radiological image annotation and reporting based on machine learning;

• Automated chest radiological image enhancement and reconstruction based on machine learning;

• Automated chest radiological image annotation and reporting based on Artificial intelligence;

• Automated chest radiological image enhancement and reconstruction based on Artificial intelligence.

Published Papers