Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: Advantages:
1. The method description is clear.

Disadvantages:
1. A small description of the intuition behind the method could be helpful.
2. Neural networks are usally sensitive to the spacing. So, changing the spacing on the inference doesn't seem to be good idea. You could change spacing on training instead, change patch size or try to reimplement the patch-wise inference function in more efficient way. 


review_title: The method is clear but more detailed intuition could be helpful

final_score: rating: 6: Marginally above acceptance threshold
review_content: Pros:
- good dice
- clear, simple method
- I would suggest to use this model as a baseline

Cons:
- method is not novel
review_title: Method is not novel, but results are not bad

final_score: rating: 5: Marginally below acceptance threshold
review_content: Pros:
- Interesting augmentation strategy
- Good papers, clear tables

Cons
- No image describing the method
- Poor section layout in method description
- Not a very original idea

Most of all, poor DSC scores, both for baseline and for proposed method. The actual baseline scores, which other papers reached, are higher than the baseline scores presented here.
review_title: Average paper

final_score: rating: 6: Marginally above acceptance threshold
review_content: 
Pros:
1. A base segmentation method for abdominal organs.
2. Bigdata and Pseudo-labeling learning is indeed useful for less annotation CT scans.

Cons:
1. The pixel values larger than 300.0 should be set into 300.0, and smaller than -300.0 should be -300.0.
2.  Advise to try coarse-to-fine framework.
3. Mixed precision work may be useful for your work
review_title: Good try and better if the baseline of labeled data could be improved

Meta-review: metareview: Figure 1 Suspected plagiarism
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