Deep Learning-Based Imaging and Sensing Technologies for Biomedical Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 4931
Special Issue Editor
Special Issue Information
Dear Colleagues,
With the advent of deep learning, Artificial Intelligence (AI) models, including convolutional neural networks (CNNs), have delivered promising results for health monitoring and detection and prediction of different diseases using biomedical imaging and sensing technologies. These technologies help to improve the overall patient outcome by providing personalized diagnostics, prognostics, and treatment, improving the quality of life of patients. The unique challenges of developing AI models for health monitoring and disease diagnosis and prognosis using imaging and sensing technologies require customized models that go beyond off-the-shelf and generic AI solutions. These challenges include high accuracy, reliability, and explainability of the AI results for biomedical applications. To bring state-of-the-art research together, research papers reporting novel AI-driven imaging and/or sensing technologies with clinical applications are invited for submission to this Special Issue. The scope and topic of this Special Issue includes but is not limited to:
- AI-driven advances in biomedical optical imaging/sensing technologies (e.g., optical imaging, optical coherence tomography, near infrared spectroscopy, diffuse optical spectroscopy) for biomedical applications;
- AI-driven advances in medical image analysis using deep learning for different imaging modalities including X-ray, CT, MRI, PET, ultrasound, etc.;
- Advances in AI-based solutions for disease diagnosis and prognosis using imaging and/or sensing technologies;
- Advances in AI explainability solutions for imaging and/or sensing technologies that address different aspects of AI explainability, including novel attention map generators as well as ways to interpret the results and integrate them into clinical settings.
Dr. Farzad Khalvati
Guest Editor
Manuscript Submission Information
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Keywords
- Biomedical imaging and sensing;
- Deep learning architectures:
- Convolutional neural networks;
- Generative adversarial networks;
- Autoencoders;
- etc.
- Attention networks;
- Explainable AI in imaging and sensing technologies;
- Reliable AI in imaging and sensing for biomedical applications;
- Explainability vs. Performance;
- Human-centric explainable AI in imaging and sensing.
