Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is considered the in vivo reference standard for assessing infarct size (IS) and microvascular obstruction (MVO) in ST-elevation myocardial infarction (STEMI) patients. Howeve...
Confounding factors inherent in medical images can significantly impact the causal exploration capabilities of deep learning models, resulting in compromised accuracy and diminished generalization performance. In this paper, we present an innovative ...
Medical image segmentation is a pivotal task within the realms of medical image analysis and computer vision. While current methods have shown promise in accurately segmenting major regions of interest, the precise segmentation of boundary areas rema...
Clinical data warehouses, which have arisen over the last decade, bring together the medical data of millions of patients and offer the potential to train and validate machine learning models in real-world scenarios. The quality of MRIs collected in ...
Medical image segmentation is crucial for computer-aided diagnosis and treatment planning, directly influencing clinical decision-making. To enhance segmentation accuracy, existing methods typically fuse local, global, and various other features. How...
Despite decades of advancements in diagnostic MRI, 30%-50% of temporal lobe epilepsy (TLE) patients remain categorized as 'non-lesional' (i.e. MRI negative) based on visual assessment by human experts. MRI-negative patients face diagnostic uncertaint...
Acute and chronic ischemic cardiomyopathy (ICM) still represents a leading cause of morbidity and mortality. Cardiac magnetic resonance (CMR) imaging plays a central role in the diagnosis and management of ICM, offering detailed visualization of card...
IEEE journal of biomedical and health informatics
Jun 1, 2025
Fully-supervised deep neural networks have achieved remarkable progress in medical image segmentation, yet they heavily rely on extensive manually labeled data and exhibit inflexibility for unseen tasks. Few-shot segmentation (FSS) addresses these is...
Background MRI protocols typically involve many imaging sequences and often require too much time. Purpose To simulate artificial intelligence (AI)-directed stratified scanning for screening breast MRI with various triage thresholds and evaluate its ...
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