74 citations · 262 across the 80 of their papers we have counts for
14 papers · 1 filter
Is visual explanation with Grad-CAM more reliable for deeper neural networks? a case study with automatic pneumothorax diagnosis
Zirui Qiu, Hassan Rivaz, Yiming Xiao
While deep learning techniques have provided the state-of-the-art performance in various clinical tasks, explainability regarding their decision-making process can greatly enhance…
VesselShot: Few-shot learning for cerebral blood vessel segmentation
Mumu Aktar, Hassan Rivaz, Marta Kersten-Oertel +1
Angiography is widely used to detect, diagnose, and treat cerebrovascular diseases. While numerous techniques have been proposed to segment the vascular network from different imag…
Robust RF Data Normalization for Deep Learning
Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
Radio frequency (RF) data contain richer information compared to other data types, such as envelope or B-mode, and employing RF data for training deep neural networks has attracted…
Frequency-Space Prediction Filtering for Phase Aberration Correction in Plane-Wave Ultrasound
Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
Ultrasound imaging often suffers from image degradation stemming from phase aberration, which represents a significant contributing factor to the overall image degradation in ultra…
Dense Error Map Estimation for MRI-Ultrasound Registration in Brain Tumor Surgery Using Swin UNETR
Soorena Salari, Amirhossein Rasoulian, Hassan Rivaz +1
Early surgical treatment of brain tumors is crucial in reducing patient mortality rates. However, brain tissue deformation (called brain shift) occurs during the surgery, rendering…
Deformable-Detection Transformer for Microbubble Localization in Ultrasound Localization Microscopy
Sepideh K. Gharamaleki, Brandon Helfield, Hassan Rivaz
To overcome the half a wavelength resolution limitations of ultrasound imaging, microbubbles (MBs) have been utilized widely in the field. Conventional MB localization methods are…
How inter-rater variability relates to aleatoric and epistemic uncertainty: a case study with deep learning-based paraspinal muscle segmentation
Parinaz Roshanzamir, Hassan Rivaz, Joshua Ahn +6
Recent developments in deep learning (DL) techniques have led to great performance improvement in medical image segmentation tasks, especially with the latest Transformer model and…
Mitigating Aberration-Induced Noise: A Deep Learning-Based Aberration-to-Aberration Approach
Mostafa Sharifzadeh, Sobhan Goudarzi, An Tang +2
One of the primary sources of suboptimal image quality in ultrasound imaging is phase aberration. It is caused by spatial changes in sound speed over a heterogeneous medium, which…
Towards multi-modal anatomical landmark detection for ultrasound-guided brain tumor resection with contrastive learning
Soorena Salari, Amirhossein Rasoulian, Hassan Rivaz +1
Homologous anatomical landmarks between medical scans are instrumental in quantitative assessment of image registration quality in various clinical applications, such as MRI-ultras…
FocalErrorNet: Uncertainty-aware focal modulation network for inter-modal registration error estimation in ultrasound-guided neurosurgery
Soorena Salari, Amirhossein Rasoulian, Hassan Rivaz +1
In brain tumor resection, accurate removal of cancerous tissues while preserving eloquent regions is crucial to the safety and outcomes of the treatment. However, intra-operative t…
Deep Ultrasound Denoising Using Diffusion Probabilistic Models
Hojat Asgariandehkordi, Sobhan Goudarzi, Adrian Basarab +1
Ultrasound images are widespread in medical diagnosis for musculoskeletal, cardiac, and obstetrical imaging due to the efficiency and non-invasiveness of the acquisition methodolog…
Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography
Md Ashikuzzaman, Ali K. Z. Tehrani, Hassan Rivaz
Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to…
Phase Aberration Correction without Reference Data: An Adaptive Mixed Loss Deep Learning Approach
Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
Phase aberration is one of the primary sources of image quality degradation in ultrasound, which is induced by spatial variations in sound speed across the heterogeneous medium. Th…
A deep learning approach for patchless estimation of ultrasound quantitative parametric image with uncertainty measurement
Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hayley Whitson +1
Quantitative ultrasound (QUS) aims to find properties of scatterers which are related to the tissue microstructure. Among different QUS parameters, scatterer number density has bee…