PURPOSE: To develop and evaluate a physics-driven, saturation contrast-aware, deep-learning-based framework for motion artifact correction in CEST MRI.
Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavil...
Accurate and timely diagnosis of brain tumors is critical for patient management and treatment planning. Magnetic resonance imaging (MRI) is a widely used modality for brain tumor detection and characterization, often aided by the administration of g...
The intracellular pH (pH) is critical for understanding various pathologies, including brain tumors. While conventional pH measurement through P-MRS suffers from low spatial resolution and long scan times, H-based APT-CEST imaging offers higher resol...
Brain tumours continue to be a primary cause of worldwide death, highlighting the critical need for effective and accurate diagnostic tools. This article presents MK-YOLOv8, an innovative lightweight deep learning framework developed for the real-tim...
AIM: To develop and validate a magnetic resonance imaging (MRI)-based deep learning radiomics model (DLRM) to predict recurrence-free survival (RFS) in lung cancer patients after surgical resection of brain metastases (BrMs).
In the healthcare field, brain tumor causes irregular development of cells in the brain. One of the popular ways to identify the brain tumor and its progression is magnetic resonance imaging (MRI). However, existing methods often suffer from high com...
Early detection of brain tumors in MRI images is vital for improving treatment results. However, deep learning models face challenges like limited dataset diversity, class imbalance, and insufficient interpretability. Most studies rely on small, sing...
Brain tumors have a great impact on patients' quality of life and accurate histopathological classification of brain tumors is crucial for patients' prognosis. Multi-instance learning (MIL) has become the mainstream method for analyzing whole-slide i...
Brain Tumor Segmentation (BraTS) plays a critical role in clinical diagnosis, treatment planning, and monitoring the progression of brain tumors. However, due to the variability in tumor appearance, size, and intensity across different MRI modalities...
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