34 citations · 48 across the 10 of their papers we have counts for
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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…
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…
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…
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…
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…