Deep Learning Techniques for Imaging-Based Diagnosis

Dear Colleagues,

Medical imaging is central to modern diagnostics, yet the growing volume and complexity of imaging data increasingly exceed what conventional interpretation workflows can sustain. Deep learning has emerged as a transformative approach, enabling automated detection, segmentation, classification, and prognostic assessment across radiology, pathology, ophthalmology, dermatology, and endoscopy. Recent advances—including transformer-based architectures, self-supervised representation learning, foundation models trained on large-scale unlabeled cohorts, and multimodal frameworks that integrate imaging with clinical and molecular data—have substantially expanded the diagnostic capabilities of these systems. Nevertheless, clinical translation remains constrained by limited annotated datasets, domain shift across institutions and scanners, insufficient external validation, and the opacity of model decision-making.

This Special Issue invites original research and review articles addressing deep learning techniques for imaging-based diagnosis. Topics of interest include, but are not limited to: novel architectures for medical image analysis; self-supervised, semi-supervised, and weakly supervised learning under limited annotation; foundation models and transfer learning; multimodal and multi-scale data integration; explainable and trustworthy AI; federated and privacy-preserving learning; domain generalization and robustness; and rigorous clinical validation studies. We particularly welcome contributions that demonstrate reproducibility, external validation, and a clear pathway toward clinical deployment.

We look forward to your contributions to this rapidly evolving field.

Prof. Dr. Yongsuk Kim
Guest Editor

Manuscript Submission Information

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