Application of Deep Learning in Medical Imaging

Dear Colleagues,

Over the past decade, deep learning has transitioned from a promising computational paradigm to a foundational engine of progress across nearly every facet of medical imaging. Convolutional neural networks (CNNs) catalyzed early breakthroughs in lesion detection and organ segmentation; transformer-based architectures and hybrid vision–language models are now redefining multi-modal reasoning; and self-supervised, federated, and foundation model approaches are beginning to close the longstanding gap between algorithmic potential and clinical adoption.

This Special Issue will focus on innovative research in the applications of deep learning in medical imaging, covering architecture designs, task applications, clinical research, and model deployment. It will emphasize large-scale dataset establishment, model structure development, trustworthy and robust deployment in real-world care pathways, and the translation of models across diverse clinical tasks, driving cutting-edge exploration and practice in medical AI technology.

Topics of interest include but, are not limited to, the following:

• Development, validation, and application of deep learning-based model in medical imaging;
• Establish of large-scale standardized clinical datasets;
• Applications in different medical modalities;
• Explainability and uncertainty research for clinical decision support;
• Self-supervised and weakly supervised learning for annotation reduction;
• Federated, privacy-preserving, and domain adaptation frameworks addressing data fragmentation.

We look forward to hearing from you.

Dr. Jingyang Zhang
Dr. Lei Ma
Guest Editors

Manuscript Submission Information

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• development, validation, and application of deep learning-based model in medical imaging
• establish of large-scale standardized clinical datasets
• applications in different medical modalities
• explainability and uncertainty research for clinical decision support
• self-supervised and weakly supervised learning for annotation reduction
• federated, privacy-preserving, and domain adaptation frameworks addressing data fragmentation