Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: Pros:
- The network speed and metric scores are balanced
- By mean teacher training and Monte Carlo sampling, the performance is improved and the model learns from unlabelled data
- The preprocessing is detailed and considerate

Cons:
- It's better to describe the post processing and explain more about cases in discussion section
review_title: Good work merging mean teacher training and SE Block with U-Net on multi-organ segmentation task

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: The description of the method is clear and easy to follow. The motivation of the method is convinsive.

Comments:
1. It would be interesting to compare the performance of your method with conventional teacher-student model to clearly see how filtering of high uncertainty predictions affect the quality of the segmentation and probably feature maps.
2. More details about the method would be helpful, for example what "unsupervised loss function" did you use?
review_title: Overall good paper

final_score: rating: 5: Marginally below acceptance threshold
review_content: Pros:
Preprocessing phase was well described   
Mean teacher framework based on VNet was used for this semi-supervised segmentation task. A inception block and a squeeze-and-excitation block was added into the VNet. 

Cons:
Mean teacher framework is a well-known method of semi-supervised learning, while the inception block and squeeze-and-excitation block are also common method. 
Why combining these two can theoretically improve the multi-organ segmentation performance was not discussed. 
Overall, the article is lack of innovations, the modifications with mean teacher framework are trivial.
review_title: A good try, but lack of innovations

final_score: rating: 6: Marginally above acceptance threshold
review_content: The authors propose an uncertainty-aware mean teacher framework with consistency regularization for semi-supervised organ segmentation.

Pros:
. Well-written paper, methods are clearly described.
. The method achieves relatively good performance on the validation set with a mean Dice of 0.7458


Cons:
. No results are presented in the abstract.
. No ablation study is performed to evaluate the individual contributions of all introduced components, namely the uncertainty-based selection of unlabeled samples and the post-processing.
. The performance using the unlabeled data improves by less than 1% compared to the fully-supervised baseline. This indicates that the proposed semi-supervised method cannot efficiently leverage the unlabelled data.
review_title: Uncertainty-aware mean teacher framework with consistency regularization for semi-supervised organ segmentation

final_score: rating: 7: Good paper, accept
review_content: Pros:
1.  A novel uncertainty-aware mean teacher framework with inception and squeeze-and-excitation block (UMT-ISE) is proposed, and which could enhance the learning accurate and reliable targets.

Cons:
1. More details of the novel framework would be useful for understanding this work, e.g., Monte Carlo Dropout, Uncertainty Map, EMA, etc. 




review_title: Good framework for the unlabeled data  

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