. Metal artifacts severely damaged human tissue information from the computed tomography (CT) image, posing significant challenges to disease diagnosis. Deep learning has been widely explored for the metal artifact reduction (MAR) task. Nevertheless,...
To assess the performance of a probabilistic deep learning based algorithm for predicting inter-fraction anatomical changes in head and neck patients.A probabilistic daily anatomy model (DAM) for head and neck patients DAM (DAM) is built on the varia...
. Tracking tumors with multi-leaf collimators and x-ray imaging can be a cost-effective motion management method to reduce internal target volume margins for lung cancer patients, sparing normal tissues while ensuring target coverage. To realize that...
OBJECTIVES: Class imbalance in datasets is one of the challenges of machine learning (ML) in medical image analysis. We employed synthetic data to overcome class imbalance when segmenting bitewing radiographs as an exemplary task for using ML.
BACKGROUND: Sparse-view computed tomography (CT) substantially reduces radiation exposure but often introduces severe artifacts that compromise image fidelity. Recent advances in deep learning for solving inverse problems have shown considerable prom...
Magnetic resonance imaging (MRI) as a critical clinical tool in medical imaging, requires a long scan time for producing high-quality MRI images. To accelerate the speed of MRI while reconstructing high-quality images with sharper edges and fewer ali...
OBJECTIVES: Convolutional Neural Networks (CNNs) have long dominated image analysis in dentistry, reaching remarkable results in a range of different tasks. However, Transformer-based architectures, originally proposed for Natural Language Processing...
OBJECTIVE: To develop dynamic MRU protocol that focuses on the bladder to capture ureteral jets and to automatically estimate frequency and duration of ureteral jets from the dynamic images.
Federated learning (FL) has shown great potential in medical image computing since it provides a decentralized learning paradigm that allows multiple clients to train a model collaboratively without privacy leakage. However, current studies have show...
Fetal brain imaging is essential for prenatal care, with ultrasound (US) and magnetic resonance imaging (MRI) providing complementary strengths. While MRI has superior soft tissue contrast, US offers portable and inexpensive screening of neurological...
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