Vision Language Model for Interpretable Medical Image Segmentation
- Developed a novel approach utilizing multi-modal vision-language models to extract semantic information from image descriptions and images, enabling accurate segmentation of diverse medical images.
- Conducted extensive evaluations of existing vision language models on multiple datasets, assessing their applicability and transferability to the medical domain.
- Explored the impact of variations in image descriptions on model performance, revealing valuable insights into the model’s responsiveness to different prompts.
Rabin Adhikari
Research Assistant
Currently researching on semi-supervised and multi-modal learning, Medical Imaging, and Natural Language Processing