[HTML][HTML] A review of uncertainty estimation and its application in medical imaging
The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …
importance. Deep learning has shown great promise in medical imaging, but the reliability …
Universeg: Universal medical image segmentation
While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …
A Review of Machine Learning's Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges
Cardiovascular disease is the leading cause of global mortality and responsible for millions
of deaths annually. The mortality rate and overall consequences of cardiac disease can be …
of deaths annually. The mortality rate and overall consequences of cardiac disease can be …
Automated detection of myocardial infarction and heart conduction disorders based on feature selection and a deep learning model
An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose
various heart-related conditions in patients. An automated diagnostic tool is required to …
various heart-related conditions in patients. An automated diagnostic tool is required to …
Application of artificial intelligence techniques for automated detection of myocardial infarction: a review
Objective. Myocardial infarction (MI) results in heart muscle injury due to receiving
insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly …
insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly …
Scribbleprompt: Fast and flexible interactive segmentation for any medical image
Semantic medical image segmentation is a crucial part of both scientific research and
clinical care. With enough labelled data, deep learning models can be trained to accurately …
clinical care. With enough labelled data, deep learning models can be trained to accurately …
RETRACTED ARTICLE: Denoising transthoracic echocardiographic images in regional wall motion abnormality using deep learning techniques
Image analysis and classification perform well in pre-processed noise-free images than in
corrupted images. Synthetic aperture radar (SAR) images, Ultrasound (US) medical images …
corrupted images. Synthetic aperture radar (SAR) images, Ultrasound (US) medical images …
Tyche: Stochastic In-Context Learning for Medical Image Segmentation
Existing learning-based solutions to medical image segmentation have two important
shortcomings. First for most new segmentation tasks a new model has to be trained or fine …
shortcomings. First for most new segmentation tasks a new model has to be trained or fine …
E-Cardiac care: A comprehensive systematic literature review
U Umar, S Nayab, R Irfan, MA Khan, A Umer - Sensors, 2022 - mdpi.com
The Internet of Things (IoT) is a complete ecosystem encompassing various communication
technologies, sensors, hardware, and software. IoT cutting-edge technologies and Artificial …
technologies, sensors, hardware, and software. IoT cutting-edge technologies and Artificial …
CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation
Accurate uncertainty estimation is a critical need for the medical imaging community. A
variety of methods have been proposed, all direct extensions of classification uncertainty …
variety of methods have been proposed, all direct extensions of classification uncertainty …