74 citations · 262 across the 80 of their papers we have counts for
7 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…
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…