Venue: MIDL 2022 Conference

Reviews:

final_score: rating: 3: Borderline
review_content: summary: The paper proposes a novel neural network layer based on the bispectrum that is locally rotation invariant, a so-called LRI layer. A small U-net is compared to the same architecture where all convolution layers are substituted with LRI layers (LRI CNN) on histopathological images. Considering the F-score, the LRI CNN performed worse while having more parameters. However, the experiments show that LRI CNNs maintain a better equivariance towards 90° rotations.
The presented idea is good, and the paper is well-crafted. Nevertheless, the benefits of LRI and overall motivations could be explained better. Furthermore, the experiments may be too few and simple to justify a full paper.
strengths: The authors present a novel approach to obtain local rotation invariance based on the bispectrum and show that the proposed LRI CNNs are robust against 90°-rotations. The paper is well written and has a substantial methods section that provides a theoretical explanation for why the bispectrum is LRI. Moreover, the concept of LRI is based on a large body of previous work.
weaknesses: Arguably, using the bispectrum is an intriguing and novel approach. Nevertheless, the paper fails to really convince that using the proposed LRI-CNN is a feasible alternative to standard CNNs in biomedical segmentation. The introduction seems relatively short to a reader not familiar with LRI operators. From reading the paper alone, it is hard to tell why LRI operators are well suited for biomedical data. It would be helpful to understand what features is the LRI-layer supposed to extract and why would such behavior be beneficial?

Furthermore, the related work and experiments suggest that rotation equivariance (RE) is a far more desirable trait since most cited deep learning approaches focus on RE, and so do the presented experiments.

The main weakness of the paper is that there are only two experiments with a relatively small U-net and only one dataset. Additionally, the paper lacks comparison with other RE- or LRI approaches. Since the authors have already published many different LRI models, why not compare the proposed LRI-CNN with the steerable filters- or power spectrum approach? Furthermore, reporting the computation time for one prediction would be interesting.

In addition, not having a thorough analysis between conventional Conv-layers and the proposed bispectral layer is a missed opportunity. For example, for which image parts do LRI-CNNs outperform the standard U-net and vice versa? Do the feature maps look any different?
detailed_comments: * The 'implementation of the LRI layer' Section is difficult to understand, and a reader trying to reproduce an LRI layer may struggle here. A few sentences explaining n, n', and N would be helpful, including the implications of a large or small N.
* In the text, "i.e" is sometimes used instead of "i.e."
* It seems that some of the Lafarge et al. 2019 citations should be Lafarge et al. 2021. 
* Table 1: which N is used here?
* What are the differences between an N=0 LRI-CNN and a standard U-net? Why does the LRI-CNN perform worse?

recommendation: Poster
review_title: null

final_score: rating: 4: Weak accept
review_content: summary: The main contribution is a local rotation invariant layer that can be combined with any network (the presented approach itself demonstrates integration with a Unet) to achieve local rotation invariance in segmentation. The paper is very well-written and the methods are clearly presented. The use of open-source dataset for benchmarking performance is also good. 
strengths: A very well-written paper with clearly presented methods and results. The developed method is novel and may provide increased robustness to rotational differences in the images. Discussion is also well-written with the limitations of the method presented. However, the paper could be improved by explaining why the method underperforms would be good. Some analysis related to using LRI with the standard Unet and the relation to increasing F-score with the parameters is presented -- this could be expanded a bit more in the discussion). 
weaknesses: Some discussion of computational demands of using LRI should be presented. To what extent does the computational demands increase and how does that compare to the accuracy gains. 
While the authors acknowledge poor performance of the proposed approach, the reasons of underperformance should be discussed and elaborated more. 
The robustness experiment could be expanded a bit more or at least explained a bit better to show how the robustness increases with increasing rotations. The use of rotation in the data augmentation is curious if the method is supposed to be rotation equivariant. This should be better explained. 
detailed_comments: In general, the paper is very well written. The methodological contributions and improvement with respect to prior work is clearly presented. The paper would benefit by presenting a few more visual example results - the example Figure 2, shows absolute difference for Unet but not for the proposed method or is it a visual artifact. Also recommend using a different color contrast for the class probability map if possible. If there are any misclassifications in either method, that is not visible. 

Please see my comments in weakness for further comments.
recommendation: Oral
review_title: null

Meta-review: metareview: This paper focuses on designing a novel locally rotation invariant CNN layer. The reviewers raised some initial questions in their comments, which were addressed by the authors in their rebuttal. All reviewers now agree that the paper is ready for publication at MIDL. Based on their recommendation, I'm happy to accept this work.
confidence: 5: The area chair is absolutely certain
Decision: Accept (Poster)