Deep Learning in Medical Image Analysis: Progress and Challenges
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 October 2025
Special Issue Editors
Interests: computational pathology; medical image processing; AI for healthcare; XAI; multimodal learning
Interests: medical image processing; XAI; multimodal learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Medical images are the most important data modality in medical diagnosis, treatment, prognosis, and medical research. Traditional recognition, detection, segmentation, and reconstruction methods for medical images based on manual feature extraction and machine learning have come a long way, and their inefficient and poorly task-matched feature representations have become bottlenecks for development. With the emergence of deep learning, a good solution to this problem has arised in end-to-end representation learning. However, existing deep learning-based medical image analysis methods suffer from difficult annotations, few samples, missing modalities, and the black-boxing of models, which leads to a certain gap between them and practical clinical applications. This Special Issue covers the following topics, but it is not limited to them:
- Multimodal representation learning in medical image analysis;
- Unsupervised domain adaptation for medical image analysis;
- Meta-learning and medical image analysis;
- Deep clustering for medical image recognition;
- Interpretable deep learning for medical image analysis;
- Weakly supervised representation learning for medical image analysis;
- The analysis and representation of high-dimensional tensors for medical image analysis.
Dr. Pan Huang
Prof. Dr. Francesco Mercaldo
Dr. Sukun Tian
Guest Editors
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Keywords
- medical image analysis
- deep learning
- multimodal representation learning
- unsupervised domain adaptation
- meta-learning
- deep clustering
- interpretable deep learning
- weakly supervised representation learning
- high-dimensional tensors
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