Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: Pros: 1. Unlabeled data is well used by weak-supervised training which has a ~8% boost in DSC. 2. Combining nnUNet and nnFormer has gained a ~2% boost in DSC compared to nnUNet only. 

Cons: 1. May consider further improvement for the efficiency of both training and inference.
review_title: May consider further improvement for the efficiency of both training and inference

final_score: rating: 7: Good paper, accept
review_content: This work use nnUNet and nnFormer to generate the pseudo labels, but i can't find the structure or description of the networks, maybe they use the default model. 
Since the nnUnet or nnFormer consume  too much RAM, which exceeds the required maximum
limit.It makes me confused that the author adopt the generic UNet in nnUNet as the predictive model to reduce the RAM, GPU consume and the running time. 
It will be better to talk about details of the adopted generic Unet.
review_title: good work but talk about the networks little.

final_score: rating: 6: Marginally above acceptance threshold
review_content: The paper adopts both nnUnet and nnFormer for training and pseudo-labels generation tasks. Then another simpler Generic Unet is trained on the original labeled data and pseudo ones. 

This paper has sufficient sections and provides detailed information for each model, loss functions and configuration they used. On top of that, they report adequate quantitative and qualitative results although some comments should be given for these results.

Having said that, reviewer does not see any significant or prominent contribution of the authors in this work since they mostly just reuse previous works.

Minor suggested improvements:
- Figure 3 should point out both good and bad cases, and should be visualized in settings (recommended by the organizer)
- Subjective comments should be given on the reported evaluation results
review_title: This paper has sufficient sections and provides detailed information for each model, loss functions and configuration they used. On top of that, they report adequate quantitative and qualitative results although some comments should be given for these results.

Meta-review: metareview: Reviewers raise many concerns and suggestions. Please address all comments in the revised manuscript.
confidence: 5: The area chair is absolutely certain
Decision: Major Revision