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Currently both the network used for the nucleus segmentation as well as boundary and foreground prediction are not robust against localized image artifact.
See https://github.com/hci-unihd/antibodie…
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Hi
I am trying to train 3D segmentation network and using the Vnet config file in the github repo.
But the result seems that it is not training at all since the loss remains all the time.
Why would…
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Hi,
I wonder whether there are ideas around to deal with 'non-dense' EM datasets. In the ISBI dataset (left) the objects are directly touching each other. In my dataset (right) there is quite a lot…
erjel updated
3 years ago
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Hello, I use the following parameters to set my profile, the data used is the BRATS17 data set, the data size is 240*240*155. The problem is that the LOSS value is always around 0.8 when training. The…
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Hi all
I am going through the code, (that works wonderfully btw, thumbs up to the Author) in an attempt to better understand it.
I am suing the pre-trained model. After reading the relevant document…
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Hi,
Because the features of my images are diverse, so I am trying to subtract the mean and divide by sd of each image. How can I realize it in fine tuning?
For my images, foreground/background is …
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Hi,thx for your pro. I‘m doing post processing after segmentation,but I do not have good ideas. You mention boundary snap in the paper,but I do not know how to use it to test the effect. Could you hel…
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It's an issue about the paper, maybe not appropriate here. But I can't get contact with the paper's author, so maybe some useful discussion here.
In the paper, three data-set used: alpha-matting, com…
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I am currently trying to train different architectures within nnUNet platform to compare with nnUNet baseline architectures. I believe it should be convenient to use same preprocessing steps before co…
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I modified @FabianIsensee implementation of 2D UNet to 3D UNet using[ the paper's description ](https://arxiv.org/abs/1606.06650) . The implementation is here: https://gist.github.com/mongoose54/c93c…