Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: + Nice visualization
+ A clear and concise description of the method
+ Interesting idea
+ Strong improvement in comparison to measured baseline score

But, the baseline score itself is somewhat small, such that your improved score is same or smaller than the baseline scores of some other papers, which is as high as around .84-.86 DSC.
review_title: Good paper, interesting ideas

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: Pros: 
1. Proposing a coarse-to-fine framework with two teacher models and a student model that combines knowledge distillation and cross teaching, a consistency regularization based on pseudo-labels, for efficient semi-supervised learning.
2. Do more work for improving DSC and NSC
3. knowledge distillation and cross teaching is strong perfomance.

Cons: Lack of analysis of algorithm acceleration time.
review_title: MICCAI-FLARE

final_score: rating: 7: Good paper, accept
review_content: Strengths: The proposed model achieves efficient semi-supervised learning and obtains a mean Dice score of 0.8429 in the validation set.
Weaknesses: 
- The ablation study results (training the proposed model with only 50 labeled cases) are not presented in the paper.
- Why do you choose patch-wise intensity normalization? Do you implement the patch-wise intensity normalization during training?
- The description of training protocols is not clear. Do you use all unlabeled data for network training? How do you choose the best model weight? Do you train the two teacher models and the student model simultaneously? How do you achieve coarse-to-fine segmentation during network training?
- It is better to add more elaborate figure captions in Figure 1 and Figure 2.
review_title: Knowledge Distillation from Cross Teaching Teachers for Efficient Semi-Supervised Abdominal Organ Segmentation in CT

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