xmindflow/FuseNet
[ISBI 2024] FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation
This project helps medical professionals and researchers automatically identify and outline specific regions within medical images, like lesions on skin or structures in lung scans. You input raw medical images, and it outputs precise segmentation maps highlighting the areas of interest, without needing extensive manual annotation for training. This is ideal for radiologists, dermatologists, or medical researchers analyzing large volumes of imagery.
No commits in the last 6 months.
Use this if you need to accurately segment features in medical images but want to avoid the time-consuming and costly process of manually annotating thousands of images for training.
Not ideal if your primary goal is general image segmentation outside of medical contexts, or if you already have large, expertly annotated datasets available for supervised learning.
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22
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5
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Nov 25, 2023
Commits (30d)
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