Call for Papers: Deep Learning for Health and Medicine

Special Issue of IEEE Intelligent Systems

Impact Factor: 6.744, CORE A

Editor-in-Chief: Longbing Cao

Submissions due: May 23, 2023

Publication: March/April 2024

Nowadays, deep learning has spread over almost all fields. In healthcare and medicine, an immense amount of data is being generated by distributed sensors and cameras, as well as multi-modal digital health platforms that support audio, video, image, and text. The availability of data from medical devices and digital record systems has greatly increased the potential for automated diagnosis. The past several years have witnessed an explosion of interest in, and a dizzyingly fast development of, computer aided medical investigations using MRI, CT, Xrays images etc. Researchers, having reached a deeper understanding of the methods and on one hand are proposing elegant ways to better integrate machine learning with neural networks in complex problems (such as image reconstruction), and on the other hand are advancing the learning algorithms themselves. Note that medical imaging data may include 2D images, image volumes, and 3D geometric data (e.g., point cloud) etc.

This special issue focuses on deep learning techniques for health and medicine, including but not limited to:

● Intelligent medical and health systems

● Novel theories and methods of deep learning for medical imaging

● Drug discovery with deep learning

● Pandemic (e.g., COVID-19) management with deep learning

● Health and medical behavior analytics with deep learning

● Medical visual question and answering

● Un/semi/weakly/fully- supervised medical data (text/images)

● Graph learning on medical data (text/images)

● Generating diagnostic reports from medical images

● Fewer Labels in clinical informatics

● Summarization of clinical information

● Knowledge transfer under various clinical environments

● Multimodal medical image analysis

● Medical image registration

● Organ and lesion segmentation/detection

● Image classification with MRI/CT/PET

● Medical image enhancement/denoising

● Learning robust medical image representation with noisy annotation

● Predicting clinical outcomes from multimodal medical data

● Anomaly detection in medical images

● Active Learning and Life-long Learning in Medical computer vision

● User/patient psychometric modeling from video, image, audio, and text

For author information and guidelines on submission criteria, please visit the IS Author Information page. Please submit papers (only full papers) through the ScholarOne system, and be sure to select this special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere.

Contact the guest editors at is2-24@computer.org

Guest Editors

● Imran Razzak, University of New South Wales (Australia)

● Xuequan Lu, Deakin University (Australia)

● Ahmed Abbasi, University of Notre Dame (USA)

● Zongyuan Ge, Monash University (Australia)

● Yuejie Zhang, Fudan University (China)

The CFP is here.