Radiology

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

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Automatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer.

BACKGROUND AND PURPOSE: White matter hyperintensities in brain MRI are key indicators of various neu...

Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images.

Hepatic cystic echinococcosis (HCE), a life-threatening liver disease, has 5 subtypes, i.e., single-...

Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images.

Detecting brain tumours (BT) early improves treatment possibilities and increases patient survival r...

Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CT.

AIM: To develop a habitat imaging method for preoperative prediction of early postoperative recurren...

ReIU: an efficient preliminary framework for Alzheimer patients based on multi-model data.

The rising incidence of Alzheimer's disease (AD) poses significant challenges to traditional diagnos...

Automatic Segmentation of Vestibular Schwannoma From MRI Using Two Cascaded Deep Learning Networks.

OBJECTIVE: Automatic segmentation and detection of vestibular schwannoma (VS) in MRI by deep learnin...

Convolutional Neural Networks for the segmentation of hippocampal structures in postmortem MRI scans.

BACKGROUND: The hippocampus plays a crucial role in memory and is one of the first structures affect...

PFSH-Net: Parallel frequency-spatial hybrid network for segmentation of kidney stones in pre-contrast computed tomography images of dogs.

Kidney stone is a common urological disease in dogs and can lead to serious complications such as py...

Diagnosis of lymph node metastasis in oral squamous cell carcinoma by an MRI-based deep learning model.

BACKGROUND: Cervical lymph node metastasis (LNM) is a well-established poor prognosticator of oral s...

Effective Dose Estimation in Computed Tomography by Machine Learning.

BACKGROUND: Computed tomography scans are widely used in everyday medical practice due to speed, ima...

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast...

Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma.

Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatm...

Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a narrative survey.

Women are disproportionately affected by chronic autoimmune diseases (AD) like systemic lupus erythe...

International multicenter validation of AI-driven ultrasound detection of ovarian cancer.

Ovarian lesions are common and often incidentally detected. A critical shortage of expert ultrasound...

Artificial Intelligence and Whole Slide Imaging Assist in Thyroid Indeterminate Cytology: A Systematic Review.

INTRODUCTION: Thyroid cytopathology, particularly in cases of atypia of undetermined significance/fo...

Attention-Guided Learning With Feature Reconstruction for Skin Lesion Diagnosis Using Clinical and Ultrasound Images.

Skin lesion is one of the most common diseases, and most categories are highly similar in morphology...

Generative Adversarial Network With Robust Discriminator Through Multi-Task Learning for Low-Dose CT Denoising.

Reducing the dose of radiation in computed tomography (CT) is vital to decreasing secondary cancer r...

SISMIK for Brain MRI: Deep-Learning-Based Motion Estimation and Model-Based Motion Correction in k-Space.

MRI, a widespread non-invasive medical imaging modality, is highly sensitive to patient motion. Desp...

Bridging MRI Cross-Modality Synthesis and Multi-Contrast Super-Resolution by Fine-Grained Difference Learning.

In multi-modal magnetic resonance imaging (MRI), the tasks of imputing or reconstructing the target ...

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