Deep learning (DL) has revolutionized the field of biomedical image processing, driving forward the capabilities of medical diagnostics with its data-driven approach. In recent years, DL has rapidly enhanced the precision and efficiency of diagnosis in clinical medicine, leveraging convolutional neural networks (CNNs) to achieve notable successes. This technology allows for nuanced identification, segmentation, and classification of medical imagery, enhancing doctors' ability to make accurate diagnoses and understand disease processes better.
This Research Topic aims to showcase cutting-edge research that harnesses deep learning for biomedical image analysis. It seeks to explore how DL technologies are currently shaping the future of diagnostics and treatment planning, translating complex image data into actionable medical insights. The focus is on the application of deep learning to improve early disease detection and the personalization of treatment, ultimately contributing to advancements in patient care and treatment efficacy.
To gather further insights into deep learning's impact on biomedical imaging, we welcome articles addressing, but not limited to, the following themes:
Advanced processing techniques for medical images
Biomedical image classification and segmentation using deep learning
Lesion target detection via medical images
Classification of lesion grade and category in medical images
Utilization of natural language processing in medical documentation analysis
Biomedical image reconstruction techniques
Automated or computer-aided disease analysis and diagnosis using deep learning algorithms
Keywords: Deep learning, Artificial intelligence, Medical image segmentation, Medical image target detection, Medical image classification, Computer assisted therapy
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
