1.9k citations · 2.4k across the 10 of their papers we have counts for
10 papers
Stack of discriminative autoencoders for multiclass anomaly detection in endoscopy images
Mohammad Reza Mohebbian, Khan A. Wahid, Paul Babyn
Wireless Capsule Endoscopy (WCE) helps physicians examine the gastrointestinal (GI) tract noninvasively. There are few studies that address pathological assessment of endoscopy ima…
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video
Mohammad Reza Mohebbian, Khan A. Wahid, Anh Dinh +1
Conventional Endoscopy (CE) and Wireless Capsule Endoscopy (WCE) are known tools for diagnosing gastrointestinal (GI) tract disorders. Detecting the anatomical location of GI tract…
Automatic classification of multiple catheters in neonatal radiographs with deep learning
Robert D. E. Henderson, Xin Yi, Scott J. Adams +1
We develop and evaluate a deep learning algorithm to classify multiple catheters on neonatal chest and abdominal radiographs. A convolutional neural network (CNN) was trained using…
Cascaded Convolutional Neural Networks with Perceptual Loss for Low Dose CT Denoising
Sepehr Ataei, Javad Alirezaie, Paul Babyn
Low Dose CT Denoising research aims to reduce the risks of radiation exposure to patients. Recently researchers have used deep learning to denoise low dose CT images with promising…
Computer-Aided Assessment of Catheters and Tubes on Radiographs: How Good is Artificial Intelligence for Assessment?
Xin Yi, Scott J. Adams, Robert D. E. Henderson +1
Catheters are the second most common abnormal finding on radiographs. The position of catheters must be assessed on all radiographs, as serious complications can arise if catheters…
Deep Learning for Low-Dose CT Denoising
Maryam Gholizadeh-Ansari, Javad Alirezaie, Paul Babyn
Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and…