AIMC Topic: Deep Learning

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Deep learning-based coronary artery motion estimation and compensation for short-scan cardiac CT.

Medical physics
PURPOSE: During a typical cardiac short scan, the heart can move several millimeters. As a result, the corresponding CT reconstructions may be corrupted by motion artifacts. Especially the assessment of small structures, such as the coronary arteries...

Automatic Hyoid Bone Tracking in Real-Time Ultrasound Swallowing Videos Using Deep Learning Based and Correlation Filter Based Trackers.

Sensors (Basel, Switzerland)
(1) Background: Ultrasound provides a radiation-free and portable method for assessing swallowing. Hyoid bone locations and displacements are often used as important indicators for the evaluation of swallowing disorders. However, this requires clinic...

Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning.

Sensors (Basel, Switzerland)
Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected early. The currently available DR treatments are limited to stoppi...

Explaining deep neural networks for knowledge discovery in electrocardiogram analysis.

Scientific reports
Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction...

Structure-based protein function prediction using graph convolutional networks.

Nature communications
The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting pro...

Anatomy-aided deep learning for medical image segmentation: a review.

Physics in medicine and biology
Deep learning (DL) has become widely used for medical image segmentation in recent years. However, despite these advances, there are still problems for which DL-based segmentation fails. Recently, some DL approaches had a breakthrough by using anatom...

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis.

A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics.

Genome research
Recent developments of single-cell RNA-seq (scRNA-seq) technologies have led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effects, which are inevitable in studies involving hu...

Deep learning for registration of region of interest in consecutive wireless capsule endoscopy frames.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Functional gastrointestinal disorders (FGIDs) are reported as worldwide gastrointestinal (GI) diseases. GI motility assessment can assist the diagnosis of patients with intestine motility dysfunction. Wireless capsule endosc...

Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast.

NeuroImage
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well-typically requiring T1-weighted images ...