Radiology

FusionNet: Dual input feature fusion network with ensemble based filter feature selection for enhanced brain tumor classification.

Brain tumors pose a significant threat to human health, require a precise and quick diagnosis for effective treatment. However, achieving high diagnostic accuracy with traditional methods remains challenging due to the complex nature of brain tumors. Recent advances in deep learning have showed potential in automating brain tumor classification using brain MRI images, offering the potential to enh...

Multi-label segmentation of carpal bones in MRI using expansion transfer learning.

The purpose of this study was to develop a robust deep learning approach trained with a smallMRI dataset for multi-label segmentation of all eight carpal bones for therapy planning and wrist dynamic analysis.A small dataset of 15 3.0-T MRI scans from five health subjects was employed within this study. The MRI data was variable with respect to the field of view (FOV), wide range of image intensity...

An 8-point scale lung ultrasound scoring network fusing local detail and global features.

Manual lung ultrasound (LUS) scoring is influenced by clinicians' subjective interpretation, leading to potential inconsistencies and misdiagnoses due...

Stacked CNN-based multichannel attention networks for Alzheimer disease detection.

Alzheimer's Disease (AD) is a progressive condition of a neurological brain disorder recognized by symptoms such as dementia, memory loss, alterations...

Multicenter study on predicting postoperative upper limb muscle strength improvement in cervical spinal cord injury patients using radiomics and deep learning.

Cervical spinal cord injury is often catastrophic, frequently leading to irreversible impairment. MRI has become the gold standard for evaluating spin...

Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning.

BACKGROUND: 18-Fluoro-deoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is a valuable imaging tool widely used in the managem...

Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning.

To enhance the diagnostic accuracy of new nodules on the surgical side after breast cancer surgery using machine learning techniques and to explore t...

Advanced image preprocessing and context-aware spatial decomposition for enhanced breast cancer segmentation.

The segmentation of breast cancer diagnosis and medical imaging contains issues such as noise, variation in contrast, and low resolutions which make i...

Diagnosis of Thyroid Nodule Malignancy Using Peritumoral Region and Artificial Intelligence: Results of Hand-Crafted, Deep Radiomics Features and Radiologists' Assessment in Multicenter Cohorts.

OBJECTIVE: To develop, test, and externally validate a hybrid artificial intelligence (AI) model based on hand-crafted and deep radiomics features ext...

Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline.

While numerous studies strive to exploit the complementary potential of MRI and PET using learning-based methods, the effective fusion of the two moda...

Z-SSMNet: Zonal-aware Self-supervised Mesh Network for prostate cancer detection and diagnosis with Bi-parametric MRI.

Bi-parametric magnetic resonance imaging (bpMRI) has become a pivotal modality in the detection and diagnosis of clinically significant prostate cance...

Machine-learning tool for classifying pulmonary hypertension via expert reader-provided CT features: An educational resource for non-dedicated radiologists.

PURPOSE: Pulmonary hypertension (PH) is a complex disease classified into five groups (I-V) by the European Society of Cardiology/European Respiratory...

Deep learning-based organ-wise dosimetry of Cu-DOTA-rituximab through only one scanning.

This study aimed to generate a delayed Cu-dotatate (DOTA)-rituximab positron emission tomography (PET) image from its early-scanned image by deep lear...

Artificial intelligence can extract important features for diagnosing axillary lymph node metastasis in early breast cancer using contrast-enhanced ultrasonography.

Contrast-enhanced ultrasound (CEUS) plays a pivotal role in the diagnosis of primary breast cancer and in axillary lymph node (ALN) metastasis. Howeve...

A Bi-modal Temporal Segmentation Network for Automated Segmentation of Focal Liver Lesions in Dynamic Contrast-enhanced Ultrasound.

OBJECTIVE: To develop and validate an automated deep learning-based model for focal liver lesion (FLL) segmentation in a dynamic contrast-enhanced ult...

The diagnostic performance of automatic B-lines detection for evaluating pulmonary edema in the emergency department among novice point-of-care ultrasound practitioners.

PURPOSE: B-lines in lung ultrasound have been a critical clue for detecting pulmonary edema. However, distinguishing B-lines from other artifacts is a...

Finger-aware Artificial Neural Network for predicting arthritis in Patients with hand pain.

Arthritis is an inflammatory condition associated with joint damage, the incidence of which is increasing worldwide. In severe cases, arthritis can re...

AI and early diagnostics: mapping fetal facial expressions through development, evolution, and 4D ultrasound.

The development of facial musculature and expressions in the human fetus represents a critical intersection of developmental biology, neurology, and e...

Reducing inference cost of Alzheimer's disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners.

While multi-modal deep learning approaches trained using magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG PET...

Ontology-guided machine learning outperforms zero-shot foundation models for cardiac ultrasound text reports.

Big data can revolutionize research and quality improvement for cardiac ultrasound. Text reports are a critical part of such analyses. Cardiac ultraso...