6 citations · 9 across the 6 of their papers we have counts for
7 papers
ResNet Structure Simplification with the Convolutional Kernel Redundancy Measure
Hongzhi Zhu, Robert Rohling, Septimiu Salcudean
Deep learning, especially convolutional neural networks, has triggered accelerated advancements in computer vision, bringing changes into our daily practice. Furthermore, the stand…
Multi-task UNet: Jointly Boosting Saliency Prediction and Disease Classification on Chest X-ray Images
Hongzhi Zhu, Robert Rohling, Septimiu Salcudean
Human visual attention has recently shown its distinct capability in boosting machine learning models. However, studies that aim to facilitate medical tasks with human visual atten…
Gaze-Guided Class Activation Mapping: Leveraging Human Attention for Network Attention in Chest X-rays Classification
Hongzhi Zhu, Septimiu Salcudean, Robert Rohling
The increased availability and accuracy of eye-gaze tracking technology has sparked attention-related research in psychology, neuroscience, and, more recently, computer vision and…
Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography
Fatemeh Taheri Dezaki, Christina Luong, Tom Ginsberg +4
In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements. However, in emergenci…
U-LanD: Uncertainty-Driven Video Landmark Detection
Mohammad H. Jafari, Christina Luong, Michael Tsang +5
This paper presents U-LanD, a framework for joint detection of key frames and landmarks in videos. We tackle a specifically challenging problem, where training labels are noisy and…
On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra-observer Variability in 2D Echocardiography Quality Assessment
Zhibin Liao, Hany Girgis, Amir Abdi +6
Uncertainty of labels in clinical data resulting from intra-observer variability can have direct impact on the reliability of assessments made by deep neural networks. In this pape…