huyquoctrinh/PEFNet
The official implementation of paper PEFNet: Positional Embedding Feature for Polyp Segmentation (MMM 2023)
This project helps medical imaging researchers or computer vision scientists improve the automated detection of polyps in colonoscopy images. By inputting medical image datasets (like Kvasir-SEG or CVC-clinic DB), it processes them to accurately outline and segment polyps. The output is a precise segmentation mask, crucial for identifying areas of concern during medical diagnosis research.
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Use this if you are a researcher in medical imaging or computer vision needing a robust method for segmenting polyps in endoscopic images to advance diagnostic tools.
Not ideal if you are a clinician looking for a ready-to-use diagnostic tool, as this is a research implementation for developing and benchmarking segmentation models.
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Language
Python
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Last pushed
Apr 05, 2023
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