Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: 
1. The paper is well structured and easy to follow. The description of the method is clear.
2. The ablation study clearly shows how all parts of the methods affect segmentation quality and inference time.
review_title: Overall good paper

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: - Strong results
- Simple yet interesting ideas
- Concise description

Very good overall, although the tables could be more readable.
review_title: Good paper, high scores

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: Pros:
A dual-light UNet was designed and CutMix strategy was used for this semi-supervised learning task. 
It was a good try for using consistent-based learning in terms of both the model and the data.  
Modified loss function was tested.
nnUNet was modified to light Unet in order to have lower time cost. 
Overall, it is a good article with enough innovations

Cons:
Advise to have more descriptions on preprocessing  phase and inference phase. 

review_title: a good article with enough innovations

final_score: rating: 7: Good paper, accept
review_content: The paper's structure and grammar are good. It's very easy to read about. And I have some suggestion for you about the format.
1. The format of  Tabel 3.,Tabel 4. and the Tabel 5. makes me look inconvenient. and you can combine them together.
2. As for the Fig3. , you can add the case without ssl.
review_title: Points are clear generally speaking, just need some minor changes

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: Advantage:
1. A great robust work about semi-supervised learning design based dual-light UNet for labeled and unlabeled data.
2.  The inference is more efficient.

Disadvantage:
1. More details of the realization of semi-supervised learning process would be much more helpful for understanding the whole train and inference, respectively.
 
review_title: Efficient framework for semi-supervised work

Meta-review: metareview: Nice paper. Please address the reviewers' comments in the revised manuscript.
confidence: 5: The area chair is absolutely certain
Decision: Minor Revision