Radiology

Latest AI and machine learning research in radiology for healthcare professionals.

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Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction.

Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis...

A Multi-Task Based Deep Learning Framework With Landmark Detection for MRI Couinaud Segmentation.

To achieve precise Couinaud liver segmentation in preoperative planning for hepatic surgery, accommo...

Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications.

BACKGROUND: In recent years, the integration of artificial intelligence (AI) techniques into medical...

Enhancing MRI brain tumor classification: A comprehensive approach integrating real-life scenario simulation and augmentation techniques.

Brain cancer poses a significant global health challenge, with mortality rates showing a concerning ...

Efficient brain tumor grade classification using ensemble deep learning models.

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The ana...

Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset.

The objective of this investigation was to improve the diagnosis of breast cancer by combining two s...

Editorial: Artificial Intelligence (AI), Digital Image Analysis, and the Future of Cancer Diagnosis and Prognosis.

On October 8 2024, the Royal Swedish Academy of Sciences announced the 2024 Nobel Prize in Physics w...

Managing Postembolization Syndrome Through a Machine Learning-Based Clinical Decision Support System: A Randomized Controlled Trial.

Although transarterial chemoembolization has improved as an interventional method for hepatocellular...

Deep Learning and Automatic Differentiation of Pancreatic Lesions in Endoscopic Ultrasound: A Transatlantic Study.

INTRODUCTION: Endoscopic ultrasound (EUS) allows for characterization and biopsy of pancreatic lesio...

ACL-DUNet: A tumor segmentation method based on multiple attention and densely connected breast ultrasound images.

Breast cancer is the most common cancer in women. Breast masses are one of the distinctive signs for...

Automated anonymization of radiology reports: comparison of publicly available natural language processing and large language models.

PURPOSE: Medical reports, governed by HIPAA regulations, contain personal health information (PHI), ...

Using interpretable deep learning radiomics model to diagnose and predict progression of early AD disease spectrum: a preliminary [F]FDG PET study.

OBJECTIVES: In this study, we propose an interpretable deep learning radiomics (IDLR) model based on...

Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis.

BACKGROUND: Acute kidney injury (AKI) and contrast-induced nephropathy (CIN) are common complication...

Machine Learning Differentiates Between Benign and Malignant Parotid Tumors With Contrast-Enhanced Ultrasound Features.

BACKGROUND: Contrast-enhanced ultrasound (CEUS) is frequently used to distinguish benign parotid tum...

Effective deep-learning brain MRI super resolution using simulated training data.

BACKGROUND: In the field of medical imaging, high-resolution (HR) magnetic resonance imaging (MRI) i...

Deep Learning Approaches for the Assessment of Germinal Matrix Hemorrhage Using Neonatal Head Ultrasound.

Germinal matrix hemorrhage (GMH) is a critical condition affecting premature infants, commonly diagn...

Prediction of femoral head collapse in osteonecrosis using deep learning segmentation and radiomics texture analysis of MRI.

BACKGROUND: Femoral head collapse is a critical pathological change and is regarded as turning point...

Early Detection of Breast Cancer in MRI Using AI.

RATIONALE AND OBJECTIVES: To develop and evaluate an AI algorithm that detects breast cancer in MRI ...

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