Comments (3)
lantiga
commented on July 27, 2024
Hi Martin, we're currently running the model on the STARE dataset. We'll be publishing results soon.
As for the entire image, we would have gladly tried it, but the DRIVE dataset only consists of 20 images (STARE is not a whole lot more). We'd need a much larger dataset of annotated cases to be successful I believe.
We have developed a ladder network / U-net hybrid internally, which (in theory) helps with semi-supervised segmentation tasks. We could take advantage of an un-annotated dataset to build up a robust whole-image net, we'll eventually go this route.
Stay tuned for the next batch of results and feel free to contribute additional experiments.
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GlastonburyC
commented on July 27, 2024
Can you detail the size of the training dataset used in the original U-net paper? I believe it's very small.
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fschi
commented on July 27, 2024
In the original U-Net paper they used 30 Images with 512x512pixels
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Related Issues (20)
- Produce the segmentation for a whole image.
- only the configuration.txt file is available in the resulting test folder
- Why is there no 1st_manual and 2nd_manual under test file after I download the DIRVE? HOT 4
- Could not get the files after running training code HOT 3
- can't find sample_input_imgs
- how to train on other database?
- how to train on my own database HOT 1
- Keras and tf version HOT 2
- TypeError: float() argument must be a string or a number, not 'TiffImageFile' HOT 2
- ImportError: cannot import name 'jaccard_similarity_score' HOT 1
- how to create overlapping patches from images
- version
- How to python run_training.py ?
- please help "ImportError: No module named visualize_util
- For those who are interested in the theoretical part of this code... the article's title of this code is "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional Neural Network".
- Why i am getting less performance than yours?
- Process finished with exit code -1073740791 (0xC0000409) HOT 1
- Trouble with model architecture HOT 1
- Any subsequent image repair work
- semi-supervised learning
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