[HTML][HTML] Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)
Uncertainty estimation in healthcare involves quantifying and understanding the inherent
uncertainty or variability associated with medical predictions, diagnoses, and treatment …
uncertainty or variability associated with medical predictions, diagnoses, and treatment …
Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review
G Quer, R Arnaout, M Henne, R Arnaout - Journal of the American College …, 2021 - jacc.org
The role of physicians has always been to synthesize the data available to them to identify
diagnostic patterns that guide treatment and follow response. Today, increasingly …
diagnostic patterns that guide treatment and follow response. Today, increasingly …
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem
J Hofmanninger, F Prayer, J Pan, S Röhrich… - European Radiology …, 2020 - Springer
Background Automated segmentation of anatomical structures is a crucial step in image
analysis. For lung segmentation in computed tomography, a variety of approaches exists …
analysis. For lung segmentation in computed tomography, a variety of approaches exists …
The fully convolutional transformer for medical image segmentation
We propose a novel transformer model, capable of segmenting medical images of varying
modalities. Challenges posed by the fine-grained nature of medical image analysis mean …
modalities. Challenges posed by the fine-grained nature of medical image analysis mean …
Machine learning on small size samples: A synthetic knowledge synthesis
Machine Learning is an increasingly important technology dealing with the growing
complexity of the digitalised world. Despite the fact, that we live in a 'Big data'world where …
complexity of the digitalised world. Despite the fact, that we live in a 'Big data'world where …
[HTML][HTML] Test-time adaptable neural networks for robust medical image segmentation
Abstract Convolutional Neural Networks (CNNs) work very well for supervised learning
problems when the training dataset is representative of the variations expected to be …
problems when the training dataset is representative of the variations expected to be …
Deep learning-based automatic segmentation of images in cardiac radiography: a promising challenge
Background Due to the advancement of medical imaging and computer technology,
machine intelligence to analyze clinical image data increases the probability of disease …
machine intelligence to analyze clinical image data increases the probability of disease …
Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …
Explainable artificial intelligence and cardiac imaging: toward more interpretable models
Artificial intelligence applications have shown success in different medical and health care
domains, and cardiac imaging is no exception. However, some machine learning models …
domains, and cardiac imaging is no exception. However, some machine learning models …
Dual convolutional neural networks for breast mass segmentation and diagnosis in mammography
Deep convolutional neural networks (CNNs) have emerged as a new paradigm for
Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis systems …
Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis systems …