CLOSED: Call for Papers: Special Issue on Deep Learning for Health and Medicine
IEEE Intelligent Systems seeks submissions for this upcoming special issue.
Important Dates
Submissions Due: 23 May 2023Publication: 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, and X-ray images. Researchers, having reached a deeper understanding of the methods, 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 (such as point cloud). 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 (such as 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
Submission Guidelines
For author information and guidelines on submission criteria, please visit the IS Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.
