Advance in Deep Learning-Based Medical Image Analysis: 2nd Edition
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 92
Special Issue Editor
Interests: image and video analysis; remote sensing; deep learning; pattern recognition; medical imaging
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Deep learning is the prominent research direction for medical image analysis. The hierarchical nature of deep models learns the complex patterns of medical images, facilitating image-based diagnostics and prognosis. Different imaging modalities, including, but not limited to, RGB, CT, MRI, X-ray, ultrasound, PETS, EEG, and mammogram, are used for inferring valuable insights about the patient’s medical condition. In addition, multi-modality- and cross-modality-based learning algorithms are also explored where the models are learned using more than a single imaging modality.
In the last decade, many algorithms have been proposed, from cell segmentation to anomaly detection, with the aim of aiding radiologists and medical doctors. However, there are many limiting factors that create barriers to the ubiquitous application of such techniques in clinical practices. The availability of a large amount of high-quality labeled data, the real-time performance bottleneck, and the accuracy of algorithms themselves are some of the key challenges that researchers currently face.
Irrespective of the type of data modality, this Special Issue of Applied Sciences, titled “Advance in Deep Learning-Based Medical Image Analysis: 2nd Edition”, is dedicated to covering the recent advancements in this domain.
Dr. Mohib Ullah
Guest Editor
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Keywords
- anomaly detection
- weakly supervised learning
- tumor segmentation
- oxygenation measurements
- desmoking
- image enhancement
- activity recognition
- classification
- semi-supervised learning
- multi- and cross-modality learning
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