About this Research Topic
In recent years, Artificial Intelligence (AI) has become a research hotspot in academia and industry. It has been successfully applied in the medical and health fields. Artificial intelligence has advantages over human beings in processing big data, complex and uncertain data, and in-depth mining data potential information. Medical data contains rich human health information, which is an important basis for doctors to make medical diagnoses. In the face of complex medical information and the growing demand for medical diagnosis, doctors' artificial interpretation exposes many shortcomings, such as easy to be affected by subjective cognition, low efficiency, and high misdiagnosis rate. With the continuous development of artificial intelligence in the segmentation of lesion area, early diagnosis of disease, anatomical structure, detection of the lesion area, and other aspects, intelligent medical treatment has gradually become possible.
From a technical point of view, medical imaging diagnosis mainly relies on image recognition and deep learning. It is expected that medical image segmentation, feature extraction, quantitative analysis, comparative through artificial intelligence can achieve image diagnosis and treatment. Artificial intelligence can help to interpret medical image data, solve the problems of low diagnostic efficiency and high misdiagnosis rate of doctors to improve the diagnostic efficiency and accuracy and reduce the labor intensity of doctors. In the past few years, major AI medical imaging companies are expanding the business radius with the continuous mature of technology. Breast cancer, stroke, and bone age testing around bone and joint have become the focus of market participants. In novel coronavirus pneumonia, AI medical imaging is involved in quantitative analysis and evaluation of the curative effect of new crown pneumonia, and it has become the key force to improve the diagnostic efficiency and quality of diagnosis.
We welcome articles focused on, but not restricted to:
• Medical imaging diagnosis based on machine learning
• Multi-task learning, namely joint image segmentation and image-based diagnosis of medical images
• Early prediction of the progression of diseases based on machine learning to predict severity.
• Exploring biomarkers for disease prediction
• Feature representation of biomedical and health informatics
• Data fusion for multi-mode biomedical and health informatics
• Application of transfer learning in medical data
Keywords: Artificial intelligence technology, medical imaging, deep learning, disease screening, pathological analysis
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