Over the recent years, we have seen artificial intelligence (AI) methods reforming the zone of medical imaging. Many AI-based models have been created and improved to related medical image analysis and interpretation. Particularly, deep learning (DL) methods have exhibited brilliant performances in the screening and diagnosing of numerous disorders and diseases. A challenge of AI-driven products is to develop more accurate diagnosis systems through DL models by taking benefits of learning patterns and relationships directly from medical imaging data.


This Special Section aims to invite original research papers that report the latest advances of medical imaging-oriented AI models. Submissions should clarify the substantive improvements on work that has already been published, accepted for publication, or submitted in parallel to other conferences or journals.


The topics of interest include but are not limited to following


Ø Advanced AI and DL models

Ø Supervised or semi-supervised learning

Ø Diagnosis using biomarkers and imaging-based methods

Ø Transfer learning methods for diagnosis and segmentation

Ø Genotype, phenotype, and pathogenesis

Ø Explainable/Trustworthy AI-based prediction, segmentation, and diagnosis

Ø Medical and healthcare equipment/resources supply chain management

Ø Wearable sensors or IoT based public health support, patient behavior and emotion monitoring

Ø VR/AR computer-aided diagnosis system

Ø 2D and 3D visualization

Ø Design and development of vaccine & targeted drug

Ø Epidemic dynamics prediction and forecast

Ø Graph neural network

Ø Computational prediction of protein structure associated with virus

Ø Socio-economic impacts of infectious disease interventions

Ø Survival and risk of recurrence estimation

Ø Recovery prediction in rehabilitation

Ø Potential therapeutics

Ø Public health system or strategies

Ø Medical image registration

Ø Radiomics