34 citations · 42 across the 14 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…