Radiology

Evaluating CNN Architectures for the Automated Detection and Grading of Modic Changes in MRI: A Comparative Study.

OBJECTIVE: Modic changes (MCs) classification system is the most widely used method in magnetic resonance imaging (MRI) for characterizing subchondral vertebral marrow changes. However, it shows a high degree of sensitivity to variations in MRI because of its semiquantitative nature. In 2021, the authors of this classification system further proposed a quantitative and reliable MC grading method. ...

Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples.

Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. Successful application of machine learning techniques for disease diagnosis, prognosis, and precision medicine, requires large amounts of data for model building and optimization. To help overcome such limitations in the context of brain MRI, we present GenMIND: a collection of generati...

Detection of three-rooted mandibular first molars on panoramic radiographs using deep learning.

This study aimed to develop a deep learning system for the detection of three-rooted mandibular first molars (MFMs) on panoramic radiographs and to as...

A multi-view prognostic model for diffuse large B-cell lymphoma based on kernel canonical correlation analysis and support vector machine.

BACKGROUND AND OBJECTIVE: Positron emission tomography/computed tomography (PET/CT) is recommended as the standard imaging modality for diffuse large ...

tDKI-Net: A Joint q-t Space Learning Network for Diffusion-Time-Dependent Kurtosis Imaging.

Time-dependent diffusion magnetic resonance imaging (TDDMRI) is useful for the non-invasive characterization of tissue microstructure. These models re...

Generalized Super-Resolution 4D Flow MRI - Using Ensemble Learning to Extend Across the Cardiovascular System.

4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular s...

Signed Curvature Graph Representation Learning of Brain Networks for Brain Age Estimation.

Graph Neural Networks (GNNs) play a pivotal role in learning representations of brain networks for estimating brain age. However, the over-squashing i...

Deep-DM: Deep-Driven Deformable Model for 3D Image Segmentation Using Limited Data.

Objective - Medical image segmentation is essential for several clinical tasks, including diagnosis, surgical and treatment planning, and image-guided...

DPFNet: Fast Reconstruction of Multi-Coil MRI Based on Dual Domain Parallel Fusion Network.

There are relatively few studies on the multi-coil reconstruction task of existing Magnetic Resonance Imaging (MRI) methods, as there are problems wit...

Topology aware multitask cascaded U-Net for cerebrovascular segmentation.

Cerebrovascular segmentation is a crucial preliminary task for many computer-aided diagnosis tools dealing with cerebrovascular pathologies. Over the ...

Comparing effects of wearable robot-assisted gait training on functional changes and neuroplasticity: A preliminary study.

Robot-assisted gait training (RAGT) is a promising technique for improving the gait ability of elderly adults and patients with gait disorders by enab...

Development and Validation of Multiparametric MRI-based Interpretable Deep Learning Radiomics Fusion Model for Predicting Lymph Node Metastasis and Prognosis in Rectal Cancer: A Two-center Study.

RATIONALE AND OBJECTIVES: To develop interpretable machine learning models that utilize deep learning (DL) and radiomics based on multiparametric Magn...

Large Language Models with Vision on Diagnostic Radiology Board Exam Style Questions.

RATIONALE AND OBJECTIVES: The expansion of large language models to process images offers new avenues for application in radiology. This study aims to...

A multimodal machine learning model for the stratification of breast cancer risk.

Machine learning models for the diagnosis of breast cancer can facilitate the prediction of cancer risk and subsequent patient management among other ...

Leveraging a Vision Transformer Model to Improve Diagnostic Accuracy of Cardiac Amyloidosis With Cardiac Magnetic Resonance.

BACKGROUND: Cardiac magnetic resonance (CMR) imaging is an important diagnostic tool for diagnosis of cardiac amyloidosis (CA). However, discriminatio...

Diagnostic Performance of Artificial Intelligence-Based Angiography-Derived Non-Hyperemic Pressure Ratio Using Pressure Wire as Reference.

BACKGROUND: The angiography-derived non-hyperemic pressure ratio (angioNHPR) is a novel index of NHPR based on artificial intelligence (AI) that does ...

Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet.

Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung...

Multi-Sensor Learning Enables Information Transfer Across Different Sensory Data and Augments Multi-Modality Imaging.

Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Curren...

Revolutionizing healthcare: a comparative insight into deep learning's role in medical imaging.

Recently, Deep Learning (DL) models have shown promising accuracy in analysis of medical images. Alzeheimer Disease (AD), a prevalent form of dementia...

Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques.

Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and ...