Radiology

RAE-Net: a multi-modal neural network based on feature fusion and evidential deep learning algorithm in predicting breast cancer subtypes on DCE-MRI.

Accurate identification of molecular subtypes in breast cancer is critical for personalized treatment. This study introduces a novel neural network model, RAE-Net, based on Multimodal Feature Fusion (MFF) and the Evidential Deep Learning Algorithm (EDLA) to improve breast cancer subtype prediction using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).A dataset of 344 patients with h...

Comparison of the impact of rectal susceptibility artifacts in prostate magnetic resonance imaging on subjective evaluation and deep learning: a two-center retrospective study.

BACKGROUND: To compare the influence of rectal susceptibility artifacts on the subjective evaluation and deep learning (DL) in prostate cancer (PCa) diagnosis.

Non-Invasive Biomarkers in the Era of Big Data and Machine Learning.

Invasive diagnostic techniques, while offering critical insights into disease pathophysiology, are often limited by high costs, procedural risks, and ...

MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach.

The firmness of meningiomas is a critical factor that impacts the surgical approach recommended for patients. The conventional approaches that couple ...

Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?

The advances in artificial intelligence (AI) technology in recent years have been remarkable, and the field of radiology is at the forefront of applyi...

Deep transfer learning radiomics for distinguishing sinonasal malignancies: a preliminary MRI study.

PURPOSE: This study aimed to assess the diagnostic accuracy of combining MRI hand-crafted (HC) radiomics features with deep transfer learning (DTL) in...

Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging.

Oral carcinoma (OC) is a toxic illness among the most general malignant cancers globally, and it has developed a gradually significant public health c...

An enhanced denoising system for mammogram images using deep transformer model with fusion of local and global features.

Image denoising is a critical problem in low-level computer vision, where the aim is to reconstruct a clean, noise-free image from a noisy input, such...

Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning.

Diffusion magnetic resonance imaging (diffusion MRI) is widely employed to probe the diffusive motion of water molecules within the tissue. Numerous d...

[MP-MRI in the evaluation of non-operative treatment response, for residual and recurrent tumor detection in head and neck cancer].

As non-surgical therapies gain acceptance in head and neck tumors, the importance of imaging has increased. New therapeutic methods (in radiation ther...

Intraoperative stenosis detection in X-ray coronary angiography via temporal fusion and attention-based CNN.

BACKGROUND AND OBJECTIVE: Coronary artery disease (CAD), the leading cause of mortality, is caused by atherosclerotic plaque buildup in the arteries. ...

AI for image quality and patient safety in CT and MRI.

Substantial endeavors have been recently dedicated to developing artificial intelligence (AI) solutions, especially deep learning-based, tailored to e...

Regional Cerebral Atrophy Contributes to Personalized Survival Prediction in Amyotrophic Lateral Sclerosis: A Multicentre, Machine Learning, Deformation-Based Morphometry Study.

OBJECTIVE: Accurate personalized survival prediction in amyotrophic lateral sclerosis is essential for effective patient care planning. This study inv...

MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging.

Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical practice. While conventional methods often focus on a ...

A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity.

Chest computed tomography (CT) scans are essential for accurately assessing the severity of the novel Coronavirus (COVID-19), facilitating appropriate...

Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts.

BACKGROUND/OBJECTIVE: Longitudinal in vivo studies of murine xenograft models are widely utilized in oncology to study cancer biology and develop ther...

Machine Learning in Intravascular Ultrasound: Validating Automated Lesion Assessment for Complex Coronary Interventions.

BACKGROUND: Intravascular ultrasound (IVUS) is essential for assessing complex coronary lesions, but remains underutilized in part due to difficulties...

Incorporating indirect MRI information in a CT-based deep learning model for prostate auto-segmentation.

BACKGROUND AND PURPOSE: Computed tomography (CT) imaging poses challenges for delineation of soft tissue structures for prostate cancer external beam ...

Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.

The integration of diverse clinical modalities such as medical imaging and the tabular data extracted from patients' Electronic Health Records (EHRs) ...

Deep learning models for differentiating three sinonasal malignancies using multi-sequence MRI.

PURPOSE: To develop MRI-based deep learning (DL) models for distinguishing sinonasal squamous cell carcinoma (SCC), adenoid cystic carcinoma (ACC) and...