Venue: MIDL 2022 Conference

Reviews:

final_score: rating: 4: Weak accept
review_content: summary: This paper proposes AdwU-Net, an efficient NAS framework to search the optimal task-specific depth and width in the U-Net backbone. Specifically, an adaptive depth and width block is designed and applied hierarchically in U-Net. In each block, the optimal number of convolutional layers and channels in each layer are directly learned from data. Also, this paper conducts an efficient architecture search and reuses the network weights of different depth and width options in a differentiable manner. Extensive experiments on three subsets of the MSD dataset show that the proposed method achieves better performances over other baselines.
strengths: 1. The paper is well-written and adequately addresses prior work.

2. This paper proposes a new segmentation framework, AdwU-Net, which can efficiently complete optimal depth and width search for U-Net. And this paper designs a memory-efficient search space for depth and width search by feature map reusing, which enables exploring more depth and width options.

3. The proposed searched task-specific architectures are incorporated in the nnU-Net framework and achieves SOTA performance in several medical image segmentation datasets.

weaknesses: In Eq.4, this paper simply combines the cross entropy loss and dice loss with equal weights, while this is not always the best choice. The paper could introduce a balanced weight here and investigate the effect of the weight.
detailed_comments: The paper claims that the proposed method achieves better performances over SOTA baselines. While in Tab.2, only three NAS-based methods are compared, and the most recent one is UXNet (Ji et al., 2020). I would suggest the authors reorganize Tab.2. More specifically, try to remove a few of the manually designed networks (3d nnU-Net a necessary one while not for the others), and add more recent NAS-based methods as baselines.  

recommendation: Poster
review_title: null

final_score: rating: 4: Weak accept
review_content: summary: This paper applied a differentiable neural architecture search to develop a segmentation framework that efficiently searches the U-Net's optimal depth and width in a specific medical mission. The author compared the proposed method to the SOTA methods and conducted an ablation study to demonstrate the rationality of the architecture. The proposed framework, AdwU-Net, can efficiently achieve better performance than the manually designed networks and easily be used in other tasks.
strengths: 1. This paper proposed an adaptive depth and width U-Net for medical image segmentation framework, which is memory-efficient and achieved notably performance.
2. The comparison experiments and ablation study shows that AdwU-Net achieves the SOTA performance in several medical image segmentation tasks.
3. The proposed method can use in other backbones for various tasks.
weaknesses: 1. There is confusion in the symbols. In formula (1), $g_l^s$ denotes the Gumbel Softmax function of $l_{th}$ depth in stage $s$, and in formula (3), $g_i^s$ is the Gumbel weight parameter of the $i_{th}$ mask; In formula (2), the $o$ was not defined.
2. Some details are not precise. In section 2.3, the author did not explain how to use the $argmax$ operation. In section 3.2, the authors analyzed little about the results of the comparison methods. In Figure 1, there are 9 blocks in the figure, but there are 11 blocks used practically.
3. The experimental description and analysis are not detailed enough. The author can mention how to conduct comparison experiments. In addition, the author can note that visualization of the results has been written in the appendix or shown the visualization figure in the main text.
recommendation: Oral
review_title: null

final_score: rating: 2: Weak reject
review_content: summary: The authors propose the use of neural architecture search to find the optimal depth and width configurations of a U-Net model to perform image segmentation. They evaluate the performance of the searched architecture on a subset of the medical segmentation decathlon showing superior performance with the discovered parameters
strengths: The solution to finding the best possible network for medical image segmentation is a difficult problem to solve and the proposed solution appears promising. Additionally, the authors provide code along with the paper to make wider adoption easier.
weaknesses: Though results look promising, the organization and of the paper makes the key concepts difficult to understand. The results section lacks details on the baseline methods as well as the underlying tasks which are used to evaluate the model.
detailed_comments: Section 2.2.1 `y^s plays` - typo? 
Section 2.2.2 is a crucial part of the paper, consider rewriting this in a clearer way 
Section 2.3 Motivating the use of the two training datasets (A and B) would help here
 Algorithm 1 probably not required
Section 3.2  `State-of-the-Arts`, `Spells` - typos. More details on the SOTA methods chosen to be used would add value
Section 3.3 What is the key takeaway of this section? If it is just to show that depth and width search is better than depth or width search alone, I don’t think it adds much value. 


recommendation: []
review_title: null

final_score: rating: 4: Weak accept
review_content: summary: This paper proposed AdwU-Net: an efficient neural architecture search framework to search the optimal task-specific depth and width in the U-Net backbone. Experiments are conducted on three subsets of the MSD datasets where AdwU-Net shows better results than other methods with less number of GPU days required for optimization.
strengths: 1) The problem explored in the paper is important as the proper design of U-Net architecture leads to a good performance.

2) The experiments conducted and the results reported seem to improve the performance while using lesser number of GPU days.
weaknesses: 1) The authors mainly focus on searching for the best parameters to control the depth and width of UNet. There are two other interesting factors which also contribute the performance of UNet: Number of channels and feature resolution change (level of UNet). Why do the authors consider only the depth and width and not these characteristics? Are there any special reasons?

2) In terms of segmentation performance improvement, the improvement is not much when compared to nnUNet.
recommendation: Poster
review_title: null

Meta-review: metareview: After rebuttal, the manuscript receives 3 weakly accept and 1 borderline. The authors have clearly clarified the issues raised by the reviewers. Overall, Most reviewers are satisfied with the response given by the authors and are glad to see that the quality of the paper has been improved substantially. 
confidence: 5: The area chair is absolutely certain
Decision: Accept (Poster)