Advanced Machine Learning Techniques for Biomedical Imaging Sensing and Healthcare Applications 2023
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 2927
Special Issue Editors
Interests: machine learning; computational intelligence; image processing; data analytics; big data; natural language processing; brain–computer interface
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; remote sensing; mineral exploration; environmental and climate sciences
Special Issues, Collections and Topics in MDPI journals
Interests: network analysis; mobile computing; web services; 4G communication; cloud computing; information security through anomaly detection
Interests: support vector machines; ELM; RVFL; KRR; machine learning techniques
Interests: recommender systems; service computing; intelligent data analytics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Biomedical and healthcare sciences are data-intensive fields requiring sophisticated data mining methods to extract knowledge from the available information. Data from both these fields present several challenges to analysis, including their high dimensionality and distribution, as well as data sources, class imbalance and low sample numbers. Although the current research in this field has shown promising results, there are still several issues requiring further attention, as follows. Feature selection methods for selecting stable sets of genes to improve predictive performance along with interpretation is one such challenge necessitating further investigation. There is also a need to explore big data in biomedical and healthcare research. An increasing number of data characterise human health care and biomedical research. Healthcare data are available in different formats, including numeric, textual reports, signals and images, and the data can be obtained from different sources.
Researchers in medical imaging and healthcare rely on the expertise of clinicians, who better understand the complex medical data for disease diagnosis. Automation of diagnosis procedures may help improve patient care and overall healthcare. Recently, advanced machine learning methods have shown promising results in biomedical and healthcare applications. Therefore, there is a need to explore novel learning methods, optimization and inference techniques for processing biomedical and healthcare data to achieve methods that perform at the same standard as clinical diagnosis. Recent advances in machine learning can be used to develop sophisticated and novel applications in biomedical and healthcare domains, further attracting healthcare practitioners who have access to interesting sources of data but lack the expertise in using machine learning techniques. Special attention will be devoted to handle feature selection, class imbalance, model robustness, scalability, distributed and heterogenous data sources and data fusion in biomedical and healthcare applications.
Topics:
The main topics of this Special Issue include, but are not limited to, the following:
- Information fusion and knowledge transfer in biomedical and healthcare applications;
- Information retrieval of medical images;
- Imaging sensing tools, technologies and applications in biomedical research;
- Body motion and pose detection in biomedical imaging;
- Computer-aided detection and diagnosis, especially for cancers;
- Transfer learning in medical imaging;
- Adversarial training in medical imaging;
- Medical image reconstruction;
- Knowledge-assisted image processing;
- Domain adaptation in medical imaging;
- Content-based information retrieval ;
- Medical image compression;
- Distributed training, learning and inference for biomedical and healthcare data;
- Distributed model optimization for biomedical and healthcare data;
- Federated learning for biomedical and healthcare data
Dr. Mukesh Prasad
Dr. Rohitash Chandra
Dr. Poongodi Manoharan
Dr. Deepak Gupta
Prof. Dr. Jian Cao
Guest Editors
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