xmindflow/DAEFormer
[MICCAI 2023] DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation
This project helps medical professionals and researchers accurately identify and outline specific organs, lesions, or other structures within medical images, such as MRI or CT scans. You input a medical image dataset, and it outputs precise segmented images, highlighting the regions of interest. It's designed for medical imaging specialists, radiologists, and clinical researchers who need to analyze anatomical structures or pathologies with high precision.
126 stars. No commits in the last 6 months.
Use this if you need an efficient and accurate way to automatically delineate anatomical structures or abnormalities in medical images without extensive pre-training.
Not ideal if your primary goal is general computer vision object detection or image classification outside of medical segmentation tasks.
Stars
126
Forks
13
Language
Python
License
MIT
Category
Last pushed
Oct 12, 2023
Commits (30d)
0
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