MedSegBench is a comprehensive benchmark designed to evaluate deep learning models for medical image segmentation across a wide range of modalities. It covers a wide range of modalities, including 35 datasets with over 60,000 images from ultrasound, ...
Journal of medical imaging and radiation sciences
Nov 22, 2024
INTRODUCTION: Artificial Intelligence (AI) has the potential to transform medical imaging and radiotherapy; both fields where radiographers' use of AI tools is increasing. This study aimed to explore the views of those professionals who are now using...
In this letter to the editor, authors highlight the key role of data labeling in training AI models for medical imaging, discussing the complexities, resource demands, costs, and the relevance of quality control in the labeling process including the ...
Accurate image interpretation is essential in the field of radiology to the healthcare team in order to provide optimal patient care. This article discusses the use of artificial intelligence (AI) confidence levels to enhance the accuracy and dependa...
Persistence images, derived from topological data analysis, emerge as a powerful tool for visualizing and mitigating biases in radiological data interpretation and AI model development. This technique transforms complex topological features into stab...
The rapid advancement of technology has brought significant changes to various fields, including medical imaging (MI). This discussion paper explores the integration of computing technologies (e.g. Python and MATLAB), digital image processing (e.g. i...
IEEE journal of biomedical and health informatics
Nov 6, 2024
Recently, federated learning has become a powerful technique for medical image classification due to its ability to utilize datasets from multiple clinical clients while satisfying privacy constraints. However, there are still some obstacles in feder...
Unlike in the field of visual scene recognition, where tremendous advances have taken place due to the availability of very large datasets to train deep neural networks, inference from medical images is often hampered by the fact that only small amou...
Biomedical physics & engineering express
Nov 5, 2024
With the development of deep learning in the field of medical image segmentation, various network segmentation models have been developed. Currently, the most common network models in medical image segmentation can be roughly categorized into pure co...
Medical science monitor : international medical journal of experimental and clinical research
Nov 1, 2024
On October 8 2024, the Royal Swedish Academy of Sciences announced the 2024 Nobel Prize in Physics was awarded to Hopfield and Hinton for their foundation research on machine learning with artificial neural networks, which resulted in the current app...
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