74 citations · 262 across the 80 of their papers we have counts for
86 papers
Recursive Vision Language Models for General Symbolic Reasoning
Omid Nejati Manzari, Guillaume Lajoie, Hassan Rivaz
Hard symbolic-reasoning tasks such as Sudoku, maze pathfinding, and ARC remain challenging for LLMs due to their fixed-depth autoregressive reasoning, which limits systematic searc…
Evi-Steer: Learning to Steer Biomedical Vision-Language Models through Efficient and Generalizable Evidential Tuning
Taha Koleilat, Hassan Rivaz, Yiming Xiao
Parameter-efficient adaptation of vision-language foundation models is crucial for precise multimodal understanding of biomedical images, yet existing methods remain deterministic…
VesselSim: learning 3D blood vessel segmentation without expert annotations
Erin Rainville, Melissa Ananian, Tristan Mirolla +2
Blood vessel segmentation is a core task in medical image analysis for the care of vascular diseases and surgical planning, yet the challenges of providing expert vascular annotati…
Sparse Spectral LoRA: Routed Experts for Medical VLMs
Omid Nejati Manzari, Hojat Asgariandehkordi, Taha Koleilat +2
Large vision-language models (VLMs) excel on general benchmarks but often lack robustness in medical imaging, where heterogeneous supervision induces cross-dataset interference and…
Longitudinal Lesion Inpainting in Brain MRI via 3D Region Aware Diffusion
Zahra Karimaghaloo, Dumitru Fetco, Haz-Edine Assemlal +2
Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines. While deep generative models have shown promise in i…
MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation
Taha Koleilat, Hojat Asgariandehkordi, Omid Nejati Manzari +3
Medical image segmentation remains challenging due to limited annotations for training, ambiguous anatomical features, and domain shifts. While vision-language models such as CLIP…
MonoLoss: A Training Objective for Interpretable Monosemantic Representations
Ali Nasiri-Sarvi, Anh Tien Nguyen, Hassan Rivaz +2
Sparse autoencoders (SAEs) decompose polysemantic neural representations, where neurons respond to multiple unrelated concepts, into monosemantic features that capture single, inte…
From Narrow to Wide: Autoencoding Transformers for Ultrasound Bandwidth Recovery
Sepideh KhakzadGharamaleki, Hassan Rivaz, Brandon Helfield
Conventional pulse-echo ultrasound suffers when low-cost probes deliver only narrow fractional bandwidths, elongating pulses and erasing high-frequency detail. We address this limi…
CLIP-SVD: Efficient and Interpretable Vision-Language Adaptation via Singular Values
Taha Koleilat, Hassan Rivaz, Yiming Xiao
Vision-language models (VLMs) like CLIP have shown impressive zero-shot and few-shot learning capabilities across diverse applications. However, adapting these models to new fine-g…
DINOMotion: advanced robust tissue motion tracking with DINOv2 in 2D-Cine MRI-guided radiotherapy
Soorena Salari, Catherine Spino, Laurie-Anne Pharand +4
Accurate tissue motion tracking is critical to ensure treatment outcome and safety in 2D-Cine MRI-guided radiotherapy. This is typically achieved by registration of sequential imag…
Weakly Supervised Intracranial Aneurysm Detection and Segmentation in MR angiography via Multi-task UNet with Vesselness Prior
Erin Rainville, Amirhossein Rasoulian, Hassan Rivaz +1
Intracranial aneurysms (IAs) are abnormal dilations of cerebral blood vessels that, if ruptured, can lead to life-threatening consequences. However, their small size and soft contr…
TextSAM-EUS: Text Prompt Learning for SAM to Accurately Segment Pancreatic Tumor in Endoscopic Ultrasound
Pascal Spiegler, Taha Koleilat, Arash Harirpoush +4
Pancreatic cancer carries a poor prognosis and relies on endoscopic ultrasound (EUS) for targeted biopsy and radiotherapy. However, the speckle noise, low contrast, and unintuitive…
SPARC: Concept-Aligned Sparse Autoencoders for Cross-Model and Cross-Modal Interpretability
Ali Nasiri-Sarvi, Hassan Rivaz, Mahdi S. Hosseini
Understanding how different AI models encode the same high-level concepts, such as objects or attributes, remains challenging because each model typically produces its own isolated…
Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models
Hamza Rasaee, Taha Koleilat, Hassan Rivaz
Accurate and generalizable object segmentation in ultrasound imaging remains a significant challenge due to anatomical variability, diverse imaging protocols, and limited annotated…
Lightweight Physics-Aware Zero-Shot Ultrasound Plane-Wave Denoising
Hojat Asgariandehkordi, Mostafa Sharifzadeh, Morteza Rezanejad +1
Ultrasound Coherent Plane-Wave Compounding (CPWC) enhances image contrast by combining echoes from multiple steered transmissions. While increasing the number of steering angles ge…
Ultrasound Image Generation using Latent Diffusion Models
Benoit Freiche, Anthony El-Khoury, Ali Nasiri-Sarvi +5
Diffusion models for image generation have been a subject of increasing interest due to their ability to generate diverse, high-quality images. Image generation has immense potenti…
Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood Attention
Omid Nejati Manzari, Hojat Asgariandehkordi, Taha Koleilat +2
Convolutional networks, transformers, hybrid models, and Mamba-based architectures have demonstrated strong performance across various medical image classification tasks. However,…
Constrained and Regularized Quantitative Ultrasound Parameter Estimation using ADMM
Ali K. Z. Tehrani, Hassan Rivaz, Ivan M. Rosado-Mendez
Regularized estimation of quantitative ultrasound (QUS) parameters, such as attenuation and backscatter coefficients, has gained research interest. Recently, the alternating direct…
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…