lingorx / mem3d Goto Github PK

对于2D interaction network(𝑓IN)的输入，论文中提到的输入是3通道，分别是灰度图片、先前的分割结果和cue map进行叠加得到3通道

但从代码（train_dextr.py、train_inter.py、train_scribble.py）中的nInputChannels来看，输入通道的数目是4。

请问怎么样理解这个4，这个4是由什么叠加而成的

Hello, dear author
Thanks very much for opening source your code!

I tried to use your code to train 2D Interactive Network, while it required some pretrained models.
Would you please provide some models?

BTW, when we train the models of 2D Interactive Network and Memory Network, are they independent from each other?
Or they should be trained in order?

As said in the paper, for the MSD dataset, lung(64/32 for train and validation), colon(126/64for train and validation). But in the code, the author only give ImageSets10(100/26 for train and validation), there is no ImageSets06.

The authors should respond to this ambiguity.

Thanks for sharing the code. It is such a solid and wonderful work.
I want to reproduce the code in my own datasets. Could you please share the pre-processing code to the repositiory?

Hi,

In the journal paper you mentioned that you will make the manual scribble annotators for MSD and KiTS publicly available. Any updates on this?

Thanks!

Could you please clarify the components of each sample in the training batch: frame, mask, num_obj, and info? I'm unsure about their sizes and types, as it's unclear how they are generated in the provided dataloader. Some things in data.py are confusing to me, and I would appreciate further explanation.

I saw in the article that you manually annotated the val sets of two tasks in MSD. This is essential information to evaluate the effect of interactive segmentation model. Can you provide the mask you manually annotated for the val sets of two tasks in MSD? Thank you very much.