Deterministic and Deep Learning-Based Image Processing for Under-Exploited Medical Sensors and Devices
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 30 September 2024 | Viewed by 2788
Special Issue Editors
Interests: medical image processing; data segmentation; monomodal/multimodal image registration; 2-D/3-D image mosaicing and 3-D data reconstruction
Interests: reconfigurable computing; smart cameras; edge computing; computer vision; machine learning
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Special Issue Information
Dear Colleagues,
Even though numerous medical devices have experienced great advances in terms of instrumentation (contrast quality, high data resolution, improved signal-to-noise ratio, innovation of new imaging modalities, etc.), there are acquisition and assistive systems whose potential is still under-exploited. The ability to more optimally exploit current technology using modern artificial intelligence-based methods is challenging.
Ultrasound, dermatoscopic, and endoscopic systems are medical devices whose potential is under-exploited. For instance, the quality of endoscopic images has greatly increased in recent years, however endoscopists still have very few automated tools to facilitate the diagnosis and follow-up of lesions in the complicated scenes, such as hollow organs. This lack of tools is also an obstacle for the exploitation of modalities (such as narrow band imaging in gastroenterology for instance) which, compared to white light, can enable an earlier detection of lesions.
Medical image processing methods nowadays tend to be systematically and completely based on deep learning methods. However, the latter are not always explainable and their superiority over deterministic (classical) methods is not always obvious, notably for hollow organ cartography (mapping) or lesion classification.
The aim of this Special Issue is twofold:
- The Special Issue will focus on all types of medical image applications and devices in which AI methods (segmentation, classification, 3D reconstruction, image mosaicing, etc.) are still limited and can enable improvement in the exploitation of various other additional imaging modalities.
- Secondly, the contributions can be based on either recent deep-learning approaches, or deterministic methods or on a combination of both. The aim here is to discuss the specific advantages and drawbacks of different solutions applicable to usability of medical data and its integration in clinically driven devices.
Prof. Dr. Christian Daul
Dr. Gilberto Ochoa-Ruiz
Dr. Sharib Ali
Guest Editors
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