34 citations · 42 across the 8 of their papers we have counts for
9 papers
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…
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…
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…