58 citations · 202 across the 10 of their papers we have counts for
7 papers · 1 filter
Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage
Ezechukwu I. Nwokedi, Rasneer S. Bains, Luc Bidaut +3
This paper presents a spatiotemporal deep learning approach for mouse behavioural classification in the home-cage. Using a series of dual-stream architectures with assorted modific…
Unsupervised detection of mouse behavioural anomalies using two-stream convolutional autoencoders
Ezechukwu I Nwokedi, Rasneer S Bains, Luc Bidaut +3
This paper explores the application of unsupervised learning to detecting anomalies in mouse video data. The two models presented in this paper are a dual-stream, 3D convolutional…
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks
Mohammadreza Soltaninejad, Lei Zhang, Tryphon Lambrou +3
In this paper, we propose a novel learning based method for automated segmentation of brain tumor in multimodal MRI images, which incorporates two sets of machine -learned and hand…
Automatic individual pig detection and tracking in surveillance videos
Lei Zhang, Helen Gray, Xujiong Ye +2
Individual pig detection and tracking is an important requirement in many video-based pig monitoring applications. However, it still remains a challenging task in complex scenes, d…
Fully Automatic Segmentation and Objective Assessment of Atrial Scars for Longstanding Persistent Atrial Fibrillation Patients Using Late Gadolinium-Enhanced MRI
Guang Yang, Xiahai Zhuang, Habib Khan +10
Purpose: Atrial fibrillation (AF) is the most common cardiac arrhythmia and is correlated with increased morbidity and mortality. It is associated with atrial fibrosis, which may b…
Deep De-Aliasing for Fast Compressive Sensing MRI
Simiao Yu, Hao Dong, Guang Yang +8
Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications in order to reduce the scanning cost and improve the patient experience. This can also pote…
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network
Mohammadreza Soltaninejad, Lei Zhang, Tryphon Lambrou +2
In this paper, we propose a novel learning based method for automated segmenta-tion of brain tumor in multimodal MRI images. The machine learned features from fully convolutional n…