OBJECTIVE: Investigation of explainable deep learning methods for graph neural networks to predict HIV infections with social network information and performing domain adaptation to evaluate model transferability across different datasets.
OBJECTIVES: To evaluate the value of a magnetic resonance imaging (MRI)-based deep learning radiomic nomogram (DLRN) for distinguishing intracranial solitary fibrous tumors (ISFTs) from angiomatous meningioma (AMs) and predicting overall survival (OS...
Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Existing quantitative approaches, like radiomics and deep learning ...
Magnetic resonance imaging (MRI) is widely used in clinical diagnosis as a safe, non-invasive, high-resolution medical imaging technology, but long scanning time has been a major challenge for this technology. The undersampling reconstruction method ...
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Oct 16, 2024
BACKGROUND: Magnetic resonance imaging (MRI) is considered the gold standard for prostate segmentation. Computed tomography (CT)-based segmentation is prone to observer bias, potentially overestimating the prostate volume by ∼ 30 % compared to MRI. H...
RATIONALE AND OBJECTIVES: The aim of this study was to evaluate the capability of an ultrasound (US)-based deep learning (DL) nomogram for predicting axillary lymph node (ALN) status after neoadjuvant chemotherapy (NAC) in breast cancer patients and ...
RATIONALE AND OBJECTIVES: To investigate the value of deep learning (DL) combined with radiomics and clinical and imaging features in predicting the Ki-67 proliferation index of soft tissue tumors (STTs).
RATIONALE AND OBJECTIVES: This study aimed to develop a deep learning (DL)-based model for detecting and diagnosing cerebral aneurysms in clinical settings, with and without human assistance.
INTRODUCTION: Original research in radiology often involves handling large datasets, data manipulation, statistical tests, and coding. Recent studies show that large language models (LLMs) can solve bioinformatics tasks, suggesting their potential in...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.