Venue: NeurIPS 2022 Challenge CellSeg

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: ### Summary
To solve the cell segmentation from diverse morphologies and impurities, the author combined attention gates with U-Net++ to segment varied sizes of cells. They improved the segmentation by using the attention gate's feature filtering, resulting in a 0.5874 F1 score.

### Strength
1. The experiments indicated that adding attention gates into U-Net++ is effective.
2. All figures were nicely drawn, and they explained the architecture clearly.

### Weakness
1. The author could add some qualitative results for attention gates, specifically to emphasize the motivation of U-Net++ with these gates.
2. The experiments need more detailed quantitative results on tuning/validation/test sets separately.
3. Did all parameters in Table 2 go through all parameters? Please tell us more details.

### Minor Comment
1. The author could tell more about the limitation of the proposed method.

review_title: Nice figures but not enough description of contributions

final_score: rating: 2: Strong rejection
review_content: Summary:   
This paper proposed a network that combines attention gates with U-Net++ to segment multi-modal cell images. The method is clearly illustrated but the improvement is not robust. And the performance of this method is far from the state-of-the-art. In my opinion, this paper should be rejected.

Pros:  
1. The authors use attention gates to improve a UNet++ model by highlighting the important area of feature maps. 

Cons:
1. The authors over-claim that their method is superior to state-of-the-art networks. However, the baseline methods in the experiment are not the state-of-the-art and the performance of their proposed method is also far from the state-of-the-art.   
2. The authors failed to investigate the latest competitive cell/nucleus segmentation methods. Thus, they also failed to reproduce and compare with the most competitive methods.   
3. Attention gate seems to an important part in the paper but its implementation details are missing. It is suggested to present attention gates via formulas.   
4. In Fig.4, why place the attention gates at these three positions? Experimental results on the setting are missing.   
5. The best result in Tab.1 is 0.5637 and the best one in Tab.2 is 0.5972. Why they are different? Are Tab.1 and Tab.2 using different testing data? These experimental details are important but they are missing. I guess Tab.1 is obtained on the official website and Tab.2 is obtained on the validation set, right?  
6. The improvement of attention gates is of poor generalization. In Tab. 2, the attention gates can bring an improvement of about 3-4% (0.597 – 0.559). However, in Tab. 1, the improvement of attention gates is about 0.1%, by comparing U-Net++ (0.5622) with Proposed method (0.5637).  
7. The details of post-processing are missing.

review_title: The paper has not well investigated the state-of-the-art and the improvement is not robust

final_score: rating: 4: Ok but not good enough - rejection
review_content: Summary:
This paper attempts to improve the U-Net++ segmentation model by combining it with attention gates. The ablation study verifies the effectiveness of the modification.

Pros:
+ The figure of the network architecture looks good.

Cons:
+ The paper's content is simple and misses some critical parts, e.,g. efficiency results.
+ The method is naive and performs relatively inferior to other cell segmentation methods.
+ The motivation for adding attention gates to U-Net++  needs to be clarified.
review_title: The paper lacks of contribution and essential results.

final_score: rating: 5: Marginally below acceptance threshold
review_content: ## Summary

The authors proposed a model that combines the attention gates with U-Net++ for cell instance segmentation with various modalities. The network can improve the segmentation performance compared to some other networks. They also show that quantity of attention gate and arithmetic of post-processing in their pipeline is important for the ultimate performance. 

## Pros

The experiments are well organized and analyzed in detail.

## Cons

1. It is better to condiser utilizing unlabeled images in the pipeline.
2. UNet, U-NetR, Swin U-NetR and U-Net++ cannot represent SOTA of cell segmentation algorithms.
3. Their pipeline does not achieve good performance, more strategies can be explored such as pretraining with public datasets.
review_title: This article does not show enough contributions and I do not recommend accepting it.

Decision: Accept