Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: null
review_content: 1: The abstract is not fully depicting your whole method; please revisit your abstract section.

2: Likewise, the whole paper has many missing components and did not fully provide an overall overview of your proposed method in precise detail.

3: There is no overall mean DSC and NSD, so we could not be aware of what actual mean DSC and NSD your method had achieved.

4: It is recommended to revise your whole manuscript and modify it accordingly. 

review_title: To some extent the manuscript is incomplete and not in proper shape, it is suggested to modify the whole manuscript.

final_score: rating: 4: Ok but not good enough - rejection
review_content: Pros:
- Describe the pre/post-processing and the metrics in detail
Cons:
- more cases and the data in table should be discussed 
- The performance of the network could be better if training more epochs
review_title: CLEF: Contrastive Learning of Equivariant Features in CT Images

final_score: rating: 6: Marginally above acceptance threshold
review_content: In Abstract, you can include your quantitative results and how much improved comparing with fully-supervised baseline. In the paper, you modified the 3D-Unet, it's good to mentioned some related works on modifications on 3D-Unet (if can please add related work).
Ablation study for modified 3D-Unet, since you modified the network, you want to find out the network performance when remove certain components and to evaluate contribution of the components.

"we manage to improve quality in comparison to a fully-supervised baseline" (from abstract). In 4 Results and discussion, mentioned it again. However, there no prove to it. That's being said, to draw a conclusion or a fact, you need to prove it. You can citate other's works that has this conclusion or run experiments to prove.
review_title: need to include ablation study

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: Advantage:

1. It is very interesting that the pretrained method could work by contrastive learning, for the multi-organ's segmentation.

Disadvantage:

1.  Really look forward to more results before and after pretrained.
2.  More details of the pre-training process would be helpful and heuristics.
review_title: Great pretrained work about contrastive learning 

final_score: rating: 7: Good paper, accept
review_content: 1. The description of the method is clear.
2. The images of the segmentation results are convinced.
review_title: clear description of the method

Meta-review: metareview: Please change 1,2,... to 1), 2) ... in sec 2.1.
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