38 citations · 43 across the 3 of their papers we have counts for
6 papers
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…
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…
Generative Adversarial Network in Medical Imaging: A Review
Xin Yi, Ekta Walia, Paul Babyn
Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probabil…
Automatic catheter detection in pediatric X-ray images using a scale-recurrent network and synthetic data
Xin Yi, Scott Adams, Paul Babyn +1
Catheters are commonly inserted life supporting devices. X-ray images are used to assess the position of a catheter immediately after placement as serious complications can arise f…
Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification
Xin Yi, Ekta Walia, Paul Babyn
Melanoma is a curable aggressive skin cancer if detected early. Typically, the diagnosis involves initial screening with subsequent biopsy and histopathological examination if nece…