Comments (2)
Hey Ikboljon,
Thanks for the question. Our main competitor during development was nnUNet - both nnUNet and MedNeXt uses deep supervision while allows for an equivalent comparison between these 2 nets.
Since a majority of the other baselines (4 out of the 6) didn't use deep supervision in their original publications - we decided to keep things uniform and not use deep supervision in all 6 model. However, you are right to point this out as this was raised in the MICCAI reviews as well - we hope to follow-up on this work and test deep-supervision to the 2 out of 6 baselines that used them in the original publication in later work.
Regards,
Saikat
from mednext.
Dear Saikat,
Thank you very much for the clarification.
Regards,
Ikboljon Sobirov
from mednext.
Related Issues (16)
- Valid accuracy HOT 4
- Problem of training. HOT 12
- about batch_size HOT 2
- replace nnunetv2's with MedNeXt v1 have some problems HOT 1
- Sharing the precise setup for your experiments HOT 1
- Unable to reproduce the results for the AMOS and BraTS datasets HOT 1
- Please provide the trained model weights HOT 2
- low training speed on S3DIS? HOT 2
- several import errors HOT 2
- nnUNet_plan_and_preprocess: command not found HOT 3
- How to train MedNeXt HOT 2
- Cannot find 'nnunet_mednext.network_architecture.custom_modules.custom_networks' HOT 2
- Excessive GPU memory consumption HOT 2
- hello, can you upload both the trained weights or the predicted results of MedNeXt on KiTS19 datasets? It may not cost your extra time. Becasue we will compare your results at qualitative and quantitative aspects. Thanks! HOT 1
- About the "self.stem = nn.Conv3d(in_channels, n_channels, kernel_size=1)" HOT 6
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