amirhossein-kz/HiFormer
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation (WACV 2023)
This project helps medical professionals, researchers, and imaging specialists accurately identify and outline different structures or abnormalities within medical images, such as organs, lesions, or cells. You input raw medical image scans (e.g., CT, MRI) and receive precisely segmented images highlighting the areas of interest. This is ideal for medical image analysts, radiologists, and scientists working with diagnostic imaging.
144 stars. No commits in the last 6 months.
Use this if you need highly accurate, automated segmentation of organs, skin lesions, or specific cell types in medical images for research, diagnosis, or treatment planning.
Not ideal if you are working with non-medical image segmentation or require a solution that doesn't involve deep learning models.
Stars
144
Forks
22
Language
Jupyter Notebook
License
MIT
Last pushed
Mar 22, 2024
Commits (30d)
0
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