Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 10: Top 5% of accepted papers, seminal paper
review_content: Pros:
- self-training strategy, a high-performance hybrid architecture consisting of CNN and Swin Transformer
- high dice
- overall, authors did a great job
review_title: Combining Self-Training and Hybrid Architecture for Semi-supervised Abdominal Organ Segmentation

final_score: rating: 10: Top 5% of accepted papers, seminal paper
review_content: Advance:
1. The experimental analysis is good, and the analysis of the use of unlabeled and labeled data is sufficiently credible.
2. The network architecture is based on CNN and transformer is very creative and has strong performance.

review_title: MICCAI-FLARE

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: The authors followed the self-training strategy and employed a high-performance hybrid architecture (PHTrans) consisting of CNN and Swin Transformer for the teacher model to generate precise pseudo labels for unlabeled data. Further, introduced them with labeled data into a two-stage segmentation framework with lightweight PHTrans for training to improve the performance and generalization ability of the model while remaining efficient. The proposed model achieved average DSC and NSD is 0.8956 and 0.9316, respectively. The quality, clarity, and description of the paper are good except for some minor issues.

On the leaderboard, you have a mean DSC of 0.8981, while this manuscript shows a mean DSC of 0.8956. Please address this accordingly.

Tables 3 and 4 are splits that are not recommended. Therefore we suggest constructing a unified table that provides a clearer view to readers. 

Make sure you use nnU-Net instead of nnUNet, as in Table 1.

Please provide authentic reference/references for “Connected component-based post-processing”

What do you mean by HSD? Did you mean NSD? If so, please change it to NSD in the abstract and in the text below Table 4. Please check the whole manuscript for English grammar and typos.

review_title: The authors followed the self-training strategy and employed a high-performance hybrid architecture (PHTrans) consisting of CNN and Swin Transformer for the teacher model to generate precise pseudo labels for unlabeled data.The proposed model achieved average DSC and NSD is 0.8956 and 0.9316, respectively. 

final_score: rating: 10: Top 5% of accepted papers, seminal paper
review_content: * In Fig 1, It seem the author didn't talk a lot about generation of pseudo labels. From the figure, I can see that the author using both nn-Unet and PHTrans-L but author didn't explain how this two network get pseudo label.

At last, great work, impressive.
review_title: Impressive work, but still have some thing confusing

final_score: rating: 7: Good paper, accept
review_content: Summary:

In this paper, the authors propose a new semi-supervised method (PHTrans) to complete the abdominal organ segmentation task through a two-stage segmentation framework.

Strengths:

A hybrid architecture (PHTrans) consisting of CNN and SWN converters is used to generate pseudo labels. All the data is then trained using lightweight PHTrans. The region of interest is located, and both global and local features are taken into account.

Weaknesses:

The ablation test was not comprehensive.
Some parameters in the article are not given a reasonable explanation.

Details:

1. It does not explain how PHTrans-L and PHTrans-S change the calculation amount, and it does not explain the effectiveness of Pure Conv Module.
2. The need for multiple outputs N1+N2 is not explained.
3. The IN operation in the Conv Block in Figure (b) is not explained.
4. In Formula (2), the input of ST module is the ADD operation, and the input of Conv module is the CONCAT operation. No explanation is given.
5. What is the shorthand for PD in Tables 3 and 4 not given.
6. The English of your manuscript must be improved before resubmission. Many sentences contain grammatical and/or spelling mistakes or are not complete sentences.


review_title: Self-Training and Hybrid Architecture for Semi-supervised Segmentation

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: Comments to the Author  

Based on previous work, the author combines self-training and hybrid architecture for organ segmentation, and achieves good accuracy and inference speed.

- Abstract line 16, "HSD" should be changed to "NSD", the same problem occurs on page 8, section 4.1, the second to last line.
- Page 2, paragraph 2, last seven lines, "have" should be changed to "has"; Change "which" to "that" in the sixth line from the bottom.
- The font in Fig. 1 is too small to read and print easily.
- The 'PD' in Table 3 should be explained, similarly, the abbreviations of organ names in Table 3 and Table 4 should be explained with remarks.
- The text of this article needs further polishing.

Please go through the paper and improve the wording.
review_title: The text of this article needs further polishing.

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