28 citations · 55 across the 14 of their papers we have counts for
14 papers
3D Ultrafast Shear Wave Absolute Vibro-Elastography using a Matrix Array Transducer
Hoda S. Hashemi, Shahed K. Mohammed, Qi Zeng +3
3D ultrasound imaging provides more spatial information compared to conventional 2D frames by considering the volumes of data. One of the main bottlenecks of 3D imaging is the long…
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…
The Kidneys Are Not All Normal: Investigating the Speckle Distributions of Transplanted Kidneys
Rohit Singla, Ricky Hu, Cailin Ringstrom +4
Modelling ultrasound speckle has generated considerable interest for its ability to characterize tissue properties. As speckle is dependent on the underlying tissue architecture, m…
The Open Kidney Ultrasound Data Set
Rohit Singla, Cailin Ringstrom, Grace Hu +4
Ultrasound, because of its low cost, non-ionizing, and non-invasive characteristics, has established itself as a cornerstone radiological examination. Research on ultrasound applic…
Quasi-Real Time Multi-Frequency 3D Shear Wave Absolute Vibro-Elastography (S-WAVE) System for Prostate
Tajwar Abrar Aleef, Julio Lobo, Ali Baghani +7
This article describes a novel quasi-real time system for quantitative and volumetric measurement of tissue elasticity in the prostate. Tissue elasticity is computed by using a loc…
Ultrafast Ultrasound Imaging for 3D Shear Wave Absolute Vibro-Elastography
Hoda S. Hashemi, Reza Zahiri Azar, Septimiu E. Salcudean +1
Shear wave absolute vibro-elastography (S-WAVE) is an imaging technique that generates steady-state shear waves inside the tissue using multi-frequency excitation from an external…
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…
Multifrequency 3D Elasticity Reconstruction withStructured Sparsity and ADMM
Shahed Mohammed, Mohammad Honarvar, Qi Zeng +4
We introduce a model-based iterative method to obtain shear modulus images of tissue using magnetic resonance elastography. The method jointly finds the displacement field that bes…
A Multiparametric Volumetric Quantitative Ultrasound Imaging Technique for Soft Tissue Characterization
Farah Deeba, Caitlin Schneider, Shahed Mohammed +5
Quantitative ultrasound (QUS) offers a non-invasive and objective way to quantify tissue health. We recently presented a spatially adaptive regularization method for reconstruction…
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…
Deep Neural Maps
Mehran Pesteie, Purang Abolmaesumi, Robert Rohling
We introduce a new unsupervised representation learning and visualization using deep convolutional networks and self organizing maps called Deep Neural Maps (DNM). DNM jointly lear…