Scaling Datasets, Annotations and Algorithms for Medical Image Segmentation
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".
Deadline for manuscript submissions: closed (31 July 2025) | Viewed by 1872
Special Issue Editors
Interests: machine learning; deep learning; medical image segmentation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Image segmentation serves as a pivotal component across a myriad of clinical applications in healthcare, encompassing disease diagnosis, treatment planning, outcome prediction, and robotic surgery, among other things. Artificial intelligence (AI) can delineate anatomical structures and localize abnormalities in medical images, but this ability requires large-scale datasets, comprehensive annotations, and cutting-edge algorithms. Several disciplines, including natural language processing (e.g., GPTs), representation learning (e.g., MAE), and image segmentation (e.g., SAMs), have witnessed the transformative power of scaling data for AI training. Nevertheless, in the field of medical image analysis, this concept remains relatively underexplored due to the inherent challenges related to data and annotation curation in a medical context. This Special Issue aims to fill this gap, with a primary focus on datasets, annotations, and algorithms for medical image segmentation. We seek the contribution of publicly available medical datasets, tackling the challenges of domain shift, transfer learning, and multi-modal learning. We encourage the creation of new annotations for a variety of organs and tumors, including partial labels, noisy labels, clinical applications, and the promotion of standardized labeling protocols. We also invite contributions that develop novel algorithms to exploit large datasets, such as high-resolution imaging and benchmarking AI models, and ultimately establish foundational models for medicine.
The topics of interest include, but are not limited to, the following:
- Deep learning architectures for medical image segmentation;
- Transfer learning and unsupervised learning in medical image segmentation;
- Multimodal and multiorgan image segmentation;
- Segmentation of pathological regions in different imaging modalities;
- Real-time image segmentation and applications in surgery;
- Interpretability and explainability in machine learning models for segmentation;
- Evaluation metrics and benchmarks in medical image segmentation;
- Robustness and reliability of machine learning models in segmentation;
- Incorporation of clinical information into machine learning models;
- Machine learning models for large-scale or high-dimensional data segmentation.
Dr. Zongwei Zhou
Dr. Jeya Maria Jose
Guest Editors
Manuscript Submission Information
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