Deep Learning and Machine Learning in Biomedical Data
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 April 2024 | Viewed by 17711
Special Issue Editors
Interests: brain; diseases; image classification; medical image processing; neurophysiology; positron emission tomography; biomedical MRI; cognition; computerised tomography; feature extraction; image segmentation; neural nets; unsupervised learning
Special Issues, Collections and Topics in MDPI journals
Interests: data mining; machine learning; deep learning; statistical analysis
Special Issues, Collections and Topics in MDPI journals
Interests: MCI; sarcopenia; frailty; physical activity; exercise; digital therapeutics; smart healthcare
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The adoption of machine learning and deep learning analytics in the bio-medical field is progressing at a rapid pace, with some applications already used in pre-clinical and clinical settings. In addition, various types of bio-medical data continue to be used, and the CNN, RNN, and transformer technologies used of analysis continue to be further developed. There are many types of bio-medical data, such as image data, pathological tissue data, waveform data, natural language data, genetic data, and voice data, etc.
In this Special Issue, we aim to collect the current research on the latest machine learning and deep learning techniques of various bio-medical data. Additionally, a hypothesis for a fusion analysis technique of various bio-medical data would be most welcome.
Dr. Do-Young Kang
Dr. Sangjin Kim
Prof. Dr. Hyuntae Park
Guest Editors
Manuscript Submission Information
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Keywords
- imaging: X-ray, US, CT, MRI, fMRI, PET, SPECT, molecular imaging, pathologic slice, cell imaging, etc
- text: EMR data, descriptive data, etc
- waveform: EEG, MEG, ECoG, ECG, voice, electrophisiologic data, etc
- genetic data