McGregorWwww/UDTransNet
This repo is the official implementation of 'Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation' which is an improved journal version of UCTransNet.
This project helps medical professionals and researchers automatically identify and outline specific structures or anomalies within medical images. By inputting raw medical scans like those from dermatology or organ imaging, it outputs precise segmentation masks that highlight areas of interest. This is ideal for radiologists, pathologists, or clinical researchers who need to accurately delineate features in medical imagery.
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Use this if you need to precisely segment structures in medical images like skin lesions or internal organs to aid diagnosis or research.
Not ideal if you are working with non-medical image segmentation or require real-time processing for immediate clinical decision-making.
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95
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Language
Python
License
MIT
Category
Last pushed
Jul 20, 2024
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