Biomedical physics & engineering express
Jun 18, 2024
Recent advancements in computational intelligence, deep learning, and computer-aided detection have had a significant impact on the field of medical imaging. The task of image segmentation, which involves accurately interpreting and identifying the c...
Progress in biomedical engineering (Bristol, England)
Jun 17, 2024
Though medical imaging has seen a growing interest in AI research, training models require a large amount of data. In this domain, there are limited sets of data available as collecting new data is either not feasible or requires burdensome resources...
Computational pathology has witnessed considerable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders. However, despite the explosive growth of generative artificial intelligence (AI)...
Journal of imaging informatics in medicine
Jun 10, 2024
Previously, the lack of a standard body part ontology has been identified as a critical deficiency needed to enable enterprise imaging. This whitepaper aims to provide a comprehensive assessment of anatomical ontologies with the aim of facilitating e...
Computer methods and programs in biomedicine
Jun 8, 2024
BACKGROUND AND OBJECTIVE: Transformer, which is notable for its ability of global context modeling, has been used to remedy the shortcomings of Convolutional neural networks (CNN) and break its dominance in medical image segmentation. However, the se...
IEEE journal of biomedical and health informatics
Jun 6, 2024
Federated learning (FL) enables collaborative training of machine learning models across distributed medical data sources without compromising privacy. However, applying FL to medical image analysis presents challenges like high communication overhea...
IEEE journal of biomedical and health informatics
Jun 6, 2024
Machine learning (ML) has revolutionized medical image-based diagnostics. In this review, we cover a rapidly emerging field that can be potentially significantly impacted by ML - eye tracking in medical imaging. The review investigates the clinical, ...
Since data scarcity and data heterogeneity are prevailing for medical images, well-trained Convolutional Neural Networks (CNNs) using previous normalization methods may perform poorly when deployed to a new site. However, a reliable model for real-wo...
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