74 citations · 262 across the 80 of their papers we have counts for
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Reliability of deep learning models for anatomical landmark detection: The role of inter-rater variability
Soorena Salari, Hassan Rivaz, Yiming Xiao
Automated detection of anatomical landmarks plays a crucial role in many diagnostic and surgical applications. Progresses in deep learning (DL) methods have resulted in significant…
Comparative Analysis of Diffusion Generative Models in Computational Pathology
Denisha Thakkar, Vincent Quoc-Huy Trinh, Sonal Varma +3
Diffusion Generative Models (DGM) have rapidly surfaced as emerging topics in the field of computer vision, garnering significant interest across a wide array of deep learning appl…
CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularization
Soorena Salari, Arash Harirpoush, Hassan Rivaz +1
Anatomical landmark detection in medical images is essential for various clinical and research applications, including disease diagnosis and surgical planning. However, manual land…
BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models
Taha Koleilat, Hojat Asgariandehkordi, Hassan Rivaz +1
Recent advancements in vision-language models (VLMs), such as CLIP, have demonstrated substantial success in self-supervised representation learning for vision tasks. However, effe…
Evaluating Detection Thresholds: The Impact of False Positives and Negatives on Super-Resolution Ultrasound Localization Microscopy
Sepideh K. Gharamaleki, Brandon Helfield, Hassan Rivaz
Super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) offers a high-resolution view of microvascular structures. Yet, ULM image quality heavily relies o…
Ensemble Learning for Microbubble Localization in Super-Resolution Ultrasound
Sepideh K. Gharamaleki, Brandon Helfield, Hassan Rivaz
Super-resolution ultrasound (SR-US) is a powerful imaging technique for capturing microvasculature and blood flow at high spatial resolution. However, accurate microbubble (MB) loc…
Uncertainty Decomposition and Error Margin Detection of Homodyned-K Distribution in Quantitative Ultrasound
Dorsa Ameri, Ali K. Z. Tehrani, Ivan M. Rosado-Mendez +1
Homodyned K-distribution (HK-distribution) parameter estimation in quantitative ultrasound (QUS) has been recently addressed using Bayesian Neural Networks (BNNs). BNNs have been s…
MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation
Taha Koleilat, Hojat Asgariandehkordi, Hassan Rivaz +1
Segmentation of anatomical structures and pathological regions in medical images is essential for modern clinical diagnosis, disease research, and treatment planning. While signifi…
Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models
Hojat Asgariandehkordi, Sobhan Goudarzi, Mostafa Sharifzadeh +2
Ultrasound plane wave imaging is a cutting-edge technique that enables high frame-rate imaging. However, one challenge associated with high frame-rate ultrasound imaging is the hig…
Vision Mamba for Classification of Breast Ultrasound Images
Ali Nasiri-Sarvi, Mahdi S. Hosseini, Hassan Rivaz
Mamba-based models, VMamba and Vim, are a recent family of vision encoders that offer promising performance improvements in many computer vision tasks. This paper compares Mamba-ba…
Vim4Path: Self-Supervised Vision Mamba for Histopathology Images
Ali Nasiri-Sarvi, Vincent Quoc-Huy Trinh, Hassan Rivaz +1
Representation learning from Gigapixel Whole Slide Images (WSI) poses a significant challenge in computational pathology due to the complicated nature of tissue structures and the…
MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation
Taha Koleilat, Hojat Asgariandehkordi, Hassan Rivaz +1
Medical image segmentation of anatomical structures and pathology is crucial in modern clinical diagnosis, disease study, and treatment planning. To date, great progress has been m…
Joint enhancement of automatic chest X-ray diagnosis and radiological gaze prediction with multi-stage cooperative learning
Zirui Qiu, Hassan Rivaz, Yiming Xiao
Purpose: As visual inspection is an inherent process during radiological screening, the associated eye gaze data can provide valuable insights into relevant clinical decisions. As…
Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches
Ali K. Z. Tehrani, Guy Cloutier, An Tang +2
Quantitative ultrasound (QUS) analyzes the ultrasound backscattered data to find the properties of scatterers that correlate with the tissue microstructure. Statistics of the envel…