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trypag / NonAdjLoss

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When the code will be released? #1

Closed JunMa11 closed 5 years ago

JunMa11 commented 5 years ago

Hi, thanks for your sharing.

When the code will be released?

Looking forward to your reply!

Best, Edward

trypag commented 5 years ago

Hi Edward, Sorry I missed your issue. We are currently extending the original work, I think the final version should be ready by the end of november. I will make sure to notify you. Best Pierre-Antoine

JunMa11 commented 5 years ago

Get it. Thanks for your reply very much.

xmengli commented 5 years ago

Hi, When the code will be released?

trypag commented 5 years ago

Hi, it should be out by the beginning of January, the article version is under redaction.

tkepp commented 5 years ago

Hey, are there any updates regarding the release of the code?

trypag commented 5 years ago

Hi @tkepp, I am currently working on it, it will be done by the end of the week. This repository will host the basic code, graph extraction from data, NonAdjLoss and a function to control the lambda parameter. I will soon open source a more general library for medical imaging with torch, composed of image readers, datasets and patch sampler.

trypag commented 5 years ago

By default it will require torch v1.0, but it can work with a lower version by commenting the code

tkepp commented 5 years ago

That sounds great! I am very excited. Thanks for the fast response.

trypag commented 5 years ago

I will be happy to help you get everything to work next week.

trypag commented 5 years ago

The code is ready but I have spotted a memory leak issue during testing, I am trying to fix it right now.

trypag commented 5 years ago

The code is out. There is a dependency to a library that I will be open-sourcing tomorrow. This library includes an example implementation of NonAdjLoss for the MICCAI 2012 brain segmentation challenge. I will post a last message here when it will be done.

trypag commented 5 years ago

The library is out https://github.com/trypag/torchmed You will find an example application of the NonAdjLoss here. Feel free to ask questions.