BRaTS2019 settings

Hi,

Thanks for your fantastic work. One question is why we preprocess the BRaST2019 into a binary problem. According to your paper (https://arxiv.org/pdf/2105.09511v3.pdf), the dataset has four classes (including b.g.). Did anyone do it before? Please share the reference.

Cheers,

Hi,
Thanks for your attention. You can read the original paper of the BraTS dataset and other relevant works or our lab papers. It's a common setting.
Best,
Xiangde.

Hi,

I've searched the BRaTS2019 related paper, and for example, in this link: https://paperswithcode.com/sota/brain-tumor-segmentation-on-brats-2019. From your paper Medical Image Segmentation Using Squeeze-and-Expansion Transformers, with the code for the BraTS proceeding in here:
https://github.com/askerlee/segtran/blob/462b206570d68ed10d8b98d740f6c920753d0958/code/dataloaders/brats_processing.py#L36
I think you've built four labels (including b.g.) in this approach. But I'm confused why you simply merge them together in here,

SSL4MIS/code/dataloaders/brats_proprecessing.py

Line 105 in e8c8b5c

lab[lab > 0] = 1

.

Could you please describe the reason for such the difference (or just simply drop me a paper link)?

Cheers

Hi,
Same response, please read the original paper of the BraTS dataset in TMI 2015 carefully.
Best,
Xiangde.

Hi,

Thanks for narrowing down the search region from "other relevant works or our lab papers" to the BRaTS 2015 original TMI paper; however, I'm asking for the semi-supervised approach that utilizes the binary labelled setting for the BRaTS2019 dataset (as a ref).

For anyone who also has this concern, this submission:
https://arxiv.org/pdf/2112.02508.pdf
could be an example of such a semi-supervised setting.

Cheers

Hi,
Again, your concern has out of the scope of this project, and this project has not a duty to provide the relevant works. Actually, the literature review is your own business, but I also provide a reference[1] for you. Finally, thanks for your attention.
[1] Shuai Chen et al. Multi-Task Attention-Based Semi-Supervised
Learning for Medical Image Segmentation, In MICCAI2019.
Best,
Xiangde.