AIMC Topic: Deep Learning

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Target-oriented deep learning-based image registration with individualized test-time adaptation.

Medical physics
BACKGROUND: A classic approach in medical image registration is to formulate an optimization problem based on the image pair of interest, and seek a deformation vector field (DVF) to minimize the corresponding objective, often iteratively. It has a c...

A Deep Learning Tool for Automated Landmark Annotation on Hip and Pelvis Radiographs.

The Journal of arthroplasty
BACKGROUND: Automatic methods for labeling and segmenting pelvis structures can improve the efficiency of clinical and research workflows and reduce the variability introduced with manual labeling. The purpose of this study was to develop a single de...

Deep Learning-Based Computer-Aided Diagnosis for Breast Lesion Classification on Ultrasound: A Prospective Multicenter Study of Radiologists Without Breast Ultrasound Expertise.

AJR. American journal of roentgenology
Computer-aided diagnosis (CAD) systems for breast ultrasound interpretation have been primarily evaluated at tertiary and/or urban medical centers by radiologists with breast ultrasound expertise. The purpose of this study was to evaluate the usefu...

Deep-learning-assisted and GPU-accelerated vector Doppler imaging with aliasing-resistant velocity estimation.

Ultrasonics
Vector flow imaging is a diagnostic ultrasound modality that is suited for the visualization of complex blood flow dynamics. One popular way of realizing vector flow imaging at high frame rates over 1000 fps is to apply multi-angle vector Doppler est...

Deep Learning-Based Image Analysis of Liver Steatosis in Mouse Models.

The American journal of pathology
The incidence of nonalcoholic fatty liver disease is a continuously growing health problem worldwide, along with obesity. Therefore, novel methods to both efficiently study the manifestation of nonalcoholic fatty liver disease and to analyze drug eff...

Development of a deep-learning model for classification of LI-RADS major features by using subtraction images of MRI: a preliminary study.

Abdominal radiology (New York)
PURPOSE: Liver Imaging Reporting and Data System (LI-RADS) is limited by interreader variability. Thus, our study aimed to develop a deep-learning model for classifying LI-RADS major features using subtraction images using magnetic resonance imaging ...

Deep Learning-Based Quantitative Assessment of Melamine and Cyanuric Acid in Pet Food Using Fourier Transform Infrared Spectroscopy.

Sensors (Basel, Switzerland)
Melamine and its derivative, cyanuric acid, are occasionally added to pet meals because of their nitrogen-rich qualities, leading to the development of several health-related issues. A nondestructive sensing technique that offers effective detection ...

Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images.

Scientific reports
In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs o...

Deep Learning Detection and Segmentation of Brain Arteriovenous Malformation on Magnetic Resonance Angiography.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The delineation of brain arteriovenous malformations (bAVMs) is crucial for subsequent treatment planning. Manual segmentation is time-consuming and labor-intensive. Applying deep learning to automatically detect and segment bAVM might he...

Observer studies of image quality of denoising reduced-count cardiac single photon emission computed tomography myocardial perfusion imaging by three-dimensional Gaussian post-reconstruction filtering and deep learning.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: The aim of this research was to asses perfusion-defect detection-accuracy by human observers as a function of reduced-counts for 3D Gaussian post-reconstruction filtering vs deep learning (DL) denoising to determine if there was improved ...