The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmentation, such as the Segment Anything Model (SAM), have revealed robust g...
Federated learning (FL) methods for multi-organ segmentation in CT scans are gaining popularity, but generally require numerous rounds of parameter exchange between a central server and clients. This repetitive sharing of parameters between server an...
Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hi...
Learning a generalizable medical image segmentation model is an important but challenging task since the unseen (testing) domains may have significant discrepancies from seen (training) domains due to different vendors and scanning protocols. Existin...
IEEE transactions on neural networks and learning systems
May 2, 2025
Segmentation of complex medical images such as vascular network and pulmonary tracheal network requires segmentation of many tiny targets on each tomographic section of the 3-D medical image volume. Although semantic segmentation of medical images ba...
IEEE transactions on neural networks and learning systems
May 2, 2025
Low-dose computed tomography (LDCT) image reconstruction techniques can reduce patient radiation exposure while maintaining acceptable imaging quality. Deep learning (DL) is widely used in this problem, but the performance of testing data (also known...
PURPOSE: Conventional quantitative susceptibility mapping (QSM) methods rely on simplified physical models that assume isotropic and homogeneous tissue properties, leading to artifacts and inaccuracies in biological tissues. This study aims to develo...
BACKGROUND: Magnetic resonance imaging (MRI) is an essential tool for medical diagnosis. However, artifacts may degrade images obtained through MRI, especially owing to patient movement. Existing methods that mitigate the artifact problem are subject...
Deep neural networks are used to accurately detect, estimate, and predict human body poses in images or videos through deep learning-based human pose estimation. However, traditional multi-person pose estimation methods face challenges due to partial...
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
May 1, 2025
This study investigated the added value of using maximum-intensity projection (MIP) images for fully automatic segmentation of lesions using deep learning (DL) in [F]FDG and [Ga]Ga-prostate-specific membrane antigen (PSMA) PET/CT scans. We used 489 ...
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