74 citations · 262 across the 80 of their papers we have counts for
18 papers · 1 filter
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…
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…
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…
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…
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,…
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…
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…
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…
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…
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…
Vision Transformer for Classification of Breast Ultrasound Images
Behnaz Gheflati, Hassan Rivaz
Medical ultrasound (US) imaging has become a prominent modality for breast cancer imaging due to its ease-of-use, low-cost and safety. In the past decade, convolutional neural netw…