Radiology

MANet: a multimodal attention convolutional neural network for brain tumor classification.

Classification of brain tumors is a difficult problem in medical imaging analysis. Over the past few years, various deep learning-based techniques have been employed for detecting and classifying tumors from Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). In this paper, a new model referred to as Multimodal Attention based Convolutional Neural Network (MANet) has been proposed for d...

A deep learning system for non-invasive breast cancer diagnosis with multimodal data.

Early and accurate diagnosis of breast cancer is critical for minimizing needle biopsies and enhancing patient outcomes and requires effective integration of multimodal information. In this article, we introduce a breast cancer intelligent non-invasive diagnosis system (BINDS) to integrate multimodal medical imaging data for breast cancer risk assessment and subtype classification. BINDS uses a tw...

Precision nutrition for the prevention and management of inflammatory bowel disease.

The rising disease burden of inflammatory bowel disease (IBD) parallels the changing dietary landscape accompanying industrialization, underscoring th...

An interpretable machine learning model integrating [18F]FDG PET/CT radiomics and clinical features for predicting perforation following chemotherapy in gastrointestinal lymphoma: a multicenter study.

BACKGROUND: Perforation following chemotherapy in gastrointestinal lymphoma (PFCGL) is a rare but severe and life-threatening complication. Early pre-...

Empowering precision source apportionment and risk management of microplastics with an optimized fingerprint-receptor model and smart platform.

Accurate source apportionment of sediment microplastics (MPs) is essential for effective ecological risk management. However, conventional receptor mo...

The evolving PI-RADS paradigm.

Prostate MRI is central to the diagnostic pathway for prostate cancer (PCa), reducing unnecessary biopsies, improving the detection of clinically sign...

Multimodal Artificial Intelligence for Early Detection and Precision Management of Inflammatory and Infiltrative Cardiomyopathies.

BACKGROUND: Inflammatory and infiltrative cardiomyopathies, including cardiac sarcoidosis, transthyretin amyloidosis, and autoimmune myocarditis, are ...

Resolving multiscale challenges in neuroscience: A frequency-aware network and benchmarks for live-cell and nuclear foci segmentation.

BACKGROUND: Neuroscience image analysis is crucial for understanding neuronal dynamics and gene expression. However, two key tasks remain challenging:...

Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools.

BACKGROUND: Conventional age-based breast cancer screening ignores substantial inter-individual risk variation, contributing to overdiagnosis, false p...

Improved delineation of the cystic artery using super-resolution deep learning reconstruction in contrast-enhanced abdominal computed tomography.

This study aimed to evaluate the image quality and delineation of the cystic artery and related abdominal vessels using Super-Resolution Deep Learning...

Multimodal Personalized Mobile Health Just-in-Time Adaptive Intervention for Occupational Stress Management: Pilot Study.

BACKGROUND: As digital health solutions gain traction, there is an urgent need for effective, person-centered stress management tools for employees. A...

Automatic computation of breast cancer biomarkers from multiple [Formula: see text] F-FDG PET image segmentation.

Neoadjuvant chemotherapy is a standard clinical practice for tumor downsizing in breast cancer, with [Formula: see text]F-FDG Positron Emission Tomogr...

Predicting categorical and continuous Alzheimer's disease outcomes from a single MRI scan.

Deep learning (DL) has shown success in predicting Alzheimer's disease (AD) diagnosis, yet continuous measures such as cognitive assessment remain cri...

A Landmark-Guided Dual-Stream Synergistic Framework for Automated Intracranial Aneurysm Detection in Magnetic Resonance Angiography.

Early and accurate detection of intracranial aneurysms (IAs) is critical for preventing rupture; however, manual interpretation of time-of-flight magn...

Artificial Intelligence in Breast Reconstruction: A Scoping Review of Pre-, Intra-, and Postoperative Applications.

BACKGROUND: Artificial intelligence (AI) has become increasingly integrated into breast reconstruction, transforming preoperative planning, intraopera...

A novel hybrid framework integrating GA-driven 3D ResUNetGAN for MRI brain tumor segmentation.

Accurate brain tumor segmentation by multi-modal MRI is crucial for diagnosis, treatment planning, and prognostic assessment. This work proposes a nov...

Spatio-temporal reconstruction of early brain developmental trajectories via self-supervised learning.

Charting the normative and atypical trajectories of the brain's rapid early-life reorganization is crucial for understanding neurodevelopment. Althoug...

Assessing the suitability of automated registration and segmentation for dosimetry calculations in SIRT treatment planning.

BACKGROUND: Selective internal radiation therapy (SIRT) increasingly relies on accurate magnetic resonance imaging (MRI) to computed tomography (CT) r...

Predictors of the Efficacy of Focused Ultrasound for Moderate-to-Severe Persistent Allergic Rhinitis: A Retrospective Cohort Study.

BACKGROUND: Focused ultrasound (FUS) has achieved favorable results in the treatment of allergic rhinitis (AR). However, some patients still have poor...

Robust deep learning for incomplete MRI sequences in glioma grading and IDH mutation status prediction: a large-scale multicenter study.

OBJECTIVES: Incomplete MRI sequences pose a significant challenge to the reliability of multiparametric MRI (mp-MRI) radiomics models. This study aime...