Radiology

Preoperative discrimination of absence or presence of myometrial invasion in endometrial cancer with an MRI-based multimodal deep learning radiomics model.

OBJECTIVE: Accurate preoperative evaluation of myometrial invasion (MI) is essential for treatment decisions in endometrial cancer (EC). However, the diagnostic accuracy of commonly utilized magnetic resonance imaging (MRI) techniques for this assessment exhibits considerable variability. This study aims to enhance preoperative discrimination of absence or presence of MI by developing and validati...

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 pyelonephritis and kidney failure. However, manual diagnosis involves a lot of burdens on radiologists and may cause human errors due to fatigue. Automated methods using deep learning models have been explored to overcome this limitation. Veterinary images present additional challenges due to the vari...

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 squamous cell carcinoma (OSCC), in which occult met...

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 erythematosus (SLE), scleroderma, rheumatoid arthritis (...

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

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learnin...

Effective Dose Estimation in Computed Tomography by Machine Learning.

BACKGROUND: Computed tomography scans are widely used in everyday medical practice due to speed, image reliability, and detectability of a wide range ...

Ultrasound Report Generation With Cross-Modality Feature Alignment via Unsupervised Guidance.

Automatic report generation has arisen as a significant research area in computer-aided diagnosis, aiming to alleviate the burden on clinicians by gen...

Generalizable Reconstruction for Accelerating MR Imaging via Federated Learning With Neural Architecture Search.

Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magneti...

Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI.

Automated breast tumor segmentation on the basis of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) has shown great promise in clini...

Segmentation and Vascular Vectorization for Coronary Artery by Geometry-Based Cascaded Neural Network.

Segmentation of the coronary artery is an important task for the quantitative analysis of coronary computed tomography angiography (CCTA) images and i...

AutoSamp: Autoencoding k-Space Sampling via Variational Information Maximization for 3D MRI.

Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the re...

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 modality share a common obstacle: the accurate mod...

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 risk. However, the use of low-dose CT (LDCT) images...

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 and appearance. Deep learning models effectively ...

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 examiners has raised concerns of unnecessary inte...

Iodine Density of Lymphoma, Metastatic SCCA, and Normal Cervical lymph nodes: A Comparative Analysis Based on DLSCT.

OBJECTIVE: To compare iodine density (ID) and contrast-enhanced attenuation value (CEAV) from dual-layer spectral computed tomography (DLSCT) scans of...

Hybrid deep learning for computational precision in cardiac MRI segmentation: Integrating Autoencoders, CNNs, and RNNs for enhanced structural analysis.

Recent advancements in cardiac imaging have been significantly enhanced by integrating deep learning models, offering transformative potential in earl...

Clinical Validation of a Deep Learning Algorithm for Automated Coronary Artery Disease Detection and Classification Using a Heterogeneous Multivendor Coronary Computed Tomography Angiography Data Set.

PURPOSE: We sought to clinically validate a fully automated deep learning (DL) algorithm for coronary artery disease (CAD) detection and classificatio...

F-18 FDG PET/CT based Preoperative Machine Learning Prediction Models for Evaluating Regional Lymph Node Metastasis Status of Patients with Colon Cancer.

OBJECTIVE: This study aimed to develop a simple machine-learning model incorporating lymph node metastasis status with F-18 Fluorodeoxyglucose positro...

WALINET: A water and lipid identification convolutional neural network for nuisance signal removal in MR spectroscopic imaging.

PURPOSE: Proton magnetic resonance spectroscopic imaging ( -MRSI) provides noninvasive spectral-spatial mapping of metabolism. However, long-standing...