Transformer-Based Deep Learning in Medical Imaging and Healthy Sensors
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 25 January 2025 | Viewed by 80
Special Issue Editors
Interests: neural networks; medical imaging; BCI applications and non-invasive bioinstrumentation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Transformer-based deep learning models, originally introduced for natural language processing, have recently shown significant potential in the field of medical imaging and healthy sensors. Depending on the core architecture of the self-attention mechanism, transformer-based models enable to weigh the importance of different parts of the input data dynamically and excel at capturing long-range dependencies. Therefore, transformers are able to understand complex structures in medical images and have been applied to various medical imaging tasks, including medical imaging sensors. The application of transformer-based models in medical imaging and healthy sensors is rapidly advancing. Transformers are expected to play an increasingly important role in improving diagnostic accuracy, accelerating medical image processing workflows, and personalizing treatment plans, thereby driving a comprehensive revolution in medical imaging technology.
This Special Issue aims to compile original research to report the recent findings in applying transformer-based deep learning models in medical imaging and healthy sensors.
Potential topics of this Special Issue include, but are not limited to, the following:
- Disease diagnostics and detection.
- Medical image segmentation.
- Medical imaging sensors.
- Medical image reconstruction and enhancement.
- Multimodal fusion learning.
- Interpretability and explainability of transformers in medical imaging.
- Advanced medical imaging techniques.
- Cross-domain transfer learning.
- Medical image generation.
- 3D medical imaging.
- Real-time medical image analysis.
Dr. Steve Ling
Dr. Juan Lyu
Guest Editors
Manuscript Submission Information
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Keywords
- transformer-based models
- medical imaging
- deep learning
- self-attention mechanism
- clinical applications
- sensors
