Pattern Recognition in Biomedical Informatics
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 18198
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Special Issue Editor
Interests: classification; pattern recognition; statistical signal processing; photography; independent component analysis; machine learning; non negative matrix factorization; biomedical signal processing; neural networks
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
One research area where new machine-learning techniques are making a huge impact is biomedicine. This is because of the general reasons behind the success of any deep-learning approach, e.g., the huge amounts of data and computational power, but also thanks to some particular characteristics in the field. For example, in some biomedical applications, not only is the supervision very expensive, but even the experts are not able to recognize some patterns in the original data. The ability of the machine-learning methods to detect and extract these informative features to solve a clinical problem (detection, classification, segmentation, etc.) is one key element to explain their success.
In this Special Issue, we are looking for contributions in any biomedical area in this direction. We are interested in how machine-learning algorithms, from traditional pattern recognition approaches to modern deep-learning ones, can be useful in any biomedical problem. In being useful, we do not just mean the typical approaches to label some biomedical data, e.g., to classify a recording as healthy or not, but also other, more innovative contributions such as to generate synthetic data. In the same way, the Special Issue is open to traditional signals such as ECG, EEG, MRI, DNA, and so on, as well as new healthcare-system applications.
Dr. Jorge Igual
Guest Editor
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Keywords
- biomedicine
- machine learning
- pattern recognition
- deep learning
- informatics
- healthcare
- clinical imaging
- artificial intelligence
- biomedical signals
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