4 papers
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…