Radiology

A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods.

In this paper, we present a new multi-objective optimization approach for segmentation of Magnetic R...

Brain tumor detection using statistical and machine learning method.

BACKGROUND AND OBJECTIVE: Brain tumor occurs because of anomalous development of cells. It is one of...

Combining convolutional neural networks and star convex cuts for fast whole spine vertebra segmentation in MRI.

BACKGROUND AND OBJECTIVE: We propose an automatic approach for fast vertebral body segmentation in t...

Artificial Neural Network Computer Tomography Perfusion Prediction of Ischemic Core.

Background and Purpose- Computed tomography perfusion (CTP) is a useful tool in the evaluation of ac...

A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images.

OBJECTIVES: This study investigated whether a deep convolutional neural network (DCNN) could provide...

Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients.

OBJECTIVES: The aim of this study was to investigate and validate the performance of individual and ...

A Hybridized ELM for Automatic Micro Calcification Detection in Mammogram Images Based on Multi-Scale Features.

Detection of masses and micro calcifications are a stimulating task for radiologists in digital mamm...

Fast learning of fiber orientation distribution function for MR tractography using convolutional neural network.

PURPOSE: In diffusion-weighted magnetic resonance imaging (DW-MRI), the fiber orientation distributi...

Segmenting brain tumors from FLAIR MRI using fully convolutional neural networks.

BACKGROUND AND OBJECTIVE: Magnetic resonance imaging (MRI) is an indispensable tool in diagnosing br...

Image Thresholding Improves 3-Dimensional Convolutional Neural Network Diagnosis of Different Acute Brain Hemorrhages on Computed Tomography Scans.

Intracranial hemorrhage is a medical emergency that requires urgent diagnosis and immediate treatmen...

Detection of smoking status from retinal images; a Convolutional Neural Network study.

Cardiovascular diseases are directly linked to smoking habits, which has both physiological and anat...

Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals.

Severe spinal cord injury (SCI) leads to skeletal muscle atrophy and adipose tissue infiltration in ...

Classifying intracranial stenosis disease severity from functional MRI data using machine learning.

Translation of many non-invasive hemodynamic MRI methods to cerebrovascular disease patients has bee...

A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction.

Background Mammographic density improves the accuracy of breast cancer risk models. However, the use...

RIANet: Recurrent interleaved attention network for cardiac MRI segmentation.

BACKGROUND: Segmentation of anatomical structures of the heart from cardiac magnetic resonance image...

Adaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders.

Current deep supervised learning methods typically require large amounts of labeled data for trainin...

Precision diagnostics based on machine learning-derived imaging signatures.

The complexity of modern multi-parametric MRI has increasingly challenged conventional interpretatio...

Denoising of 3D magnetic resonance images using a residual encoder-decoder Wasserstein generative adversarial network.

Structure-preserved denoising of 3D magnetic resonance imaging (MRI) images is a critical step in me...

Prediction of coronary thin-cap fibroatheroma by intravascular ultrasound-based machine learning.

BACKGROUND AND AIMS: Although grayscale intravascular ultrasound (IVUS) is commonly used for assessi...

Is There Value for Artificial Intelligence Applications in Molecular Imaging and Nuclear Medicine?

Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: Is registration of multiphasic images necessary?

PURPOSE: To evaluate the effect of image registration on the diagnostic performance of transfer lear...