Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The authors employ hyperbolic embeddings to capture implicit hierarchical relationships in biomedical imaging. Novel components of the proposed method are (1) a self-supervised triplet loss and (2) a gyroplane convolutional layer. They validated their method on (1) synthetic toy dataset and (2) BraTS'19 benchmark, where the proposed method demonstrated superior/competitve performance compared to other unsupervised/semi-supervised/supervised methods.
main_review: Overall, this paper proposes an interesting combination of hyperbolic embeddings and self-supervised learning, and applies it to biomedical imaging which is in need of advanced unsupervsed learning methods. As such, I would consider the result of this paper worth reporting despite of limited technical novelty. However, I have a major reservation on experimental validation of the proposed method.

[Originality]

Technical novelty of the proposed method is limited to (1) introducing self-supervised triplet loss (which is not novel by itself) and (2) generalizing the gyroplane layer (Ganea et al. 2018) to the gyroplane convolutional layer (novel but limited). Nevertheless, it is a novel exploration to adopt hyperbolic embeddings to capture implicit hierarchical structures in biomedical images.

[Quality]

The quality of the paper is okay, but more thorough experimental validation is required. The synthetic toy dataset is only a proof-of-concept that doesn't tell much about the validity of the proposed method. It's an interesting start point for research, but much more emphasis should be put on real world validation. One thing is that although the toy dataset is said to be inspired by cellular organization, it is not that similar to the actual cell in terms of the scale. The superficial/conceptual resemblance isn't really meaningful/helpful. There exist a lot of publicly available electron microscopy (EM) images of cells, so it would be interesting to see what the proposed method learns from EM cellular images.

Second, the validation on the BraTS'19 dataset is too limited. Reproducing other methods is non-trivial. There must be a sanity check on whether the used code (either original or reimplementation) and experimental setup reproduce the original numbers reported by the original authors. And that's why the public benchmark and its leaderboard exist. It's a *fair* battleground for competiting methods. Why didn't the authors try to use all modalities (not just FLAIR) and evaluate their method on the test set? Also, qaulitative inspection is largely missing. People would find it interesting to see a thorough qualitative inspection of the proposed method on real world datasets, especially given that the method is unsupervised and claimed to be useful for novel discovery.  

[Clarity]

The paper is written clearly in general, organized well, and adequately informative.

[Significance]

It is significant to advance unsupervised learning methods for biomedical imaging. Self-supervised learning was facilitated by the application of hyperbolic embeddings to capture implicit hierarchical structures, which is another significant finding. 
limitations_and_societal_impact: It doesn't seem like the authors adequately addressed the limitations of the proposed method.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: In this paper, the authors proposed to learn hyperbolic representations using 3D hyperbolic VAE with a novel gyroplane convolutional layer before 3D conv encoder. Apart from standard VAE loss, a self supervised triplet loss is also added to attract related subvolumes and repel unrelated subvolumns in the embedding space. They demonstrated their results on a biologically-inspired synthetic dataset and a brain tumor segmentation dataset, and showed that their approach achieves state-of-the-art unsupervised 3D segmentation. 
main_review: Originality: while this work can be viewed as a combination of known methods, I feel that the work contains sufficient novelty. 1) the application of the hyperbolic latent to this particular task is novel, 2) modifications to [Ganea et al, 2018] to make the gyroplane layer convolutional, a necessary step for the VAE, and 3) the self-supervised pretext task and accompanying loss. I am sold on the intuition that the hyperbolic latent has some nice properties for these types of problems.

Quality: Overall, I am impressed with the technical soundness and completeness of the work. However, I have a few questions: 
- Regarding the pretext task and triplett loss: The negative patch is a spatially distant patch, but can it be content-wise similar to the positive patch as well? In Figure 1 and Table 3, we can see that both the synthetic BraTS datasets have a higher degree of symmetry than natural images. For example in Figure 2, if a positive patch is at the top left corner, is it fair to name the patch at the bottom right corner as a negative?
- The role of the upsampling and downsampling is unclear
- Why use d=2? Unless I am reading the table in the appendix wrong, you’d benefit from a higher dimensional latent.

Clarity: I think the clarity of the work is excellent overall. Great job!

Significance: I believe the task addressed here is an important one, so from this perspective I think the work has significance. The interest to the NeurIPS community would be higher if results could be shown in another domain. Also, as a sanity-check it would be good to validate your reimplementation of Naelpa et al. [2020] and Moriya et al. [2018] by reproducing their reported results in the Appendix. It would also be preferable to report multiple runs, or better yet perform statistical tests. You may want to add [1] to your comparisons.

Other comments:
- You may want to add [2] to your related work
- Interesting that gyroconv + triplet performs worse at DICE level 3… thoughts?
- The broader impact, I believe, should be have been included in the main article

[1] Uzunova, Hristina, et al. "Unsupervised pathology detection in medical images using conditional variational autoencoders." International journal of computer assisted radiology and surgery 14.3 (2019): 451-461.

[2] Baur, Christoph, et al. "Deep autoencoding models for unsupervised anomaly segmentation in brain MR images." International MICCAI Brainlesion Workshop. Springer, Cham, 2018.
limitations_and_societal_impact: No, but it seems there is no potential negative social impact
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The authors present a novel representation for learning hierarchical relations within samples (3D image pixels) in an embedding space. A component that that enables this mapping is a novel self-supervised loss function. Results on real data indicate advantageous properties. 
main_review: * Information on hierarchical representations are often ignore in multi-class classification / image segmentation tasks.
* The concept of mapping into a hyperbolic space can be considered to be innovative, as can the loss function
* Results on exemplary application data is promising. 
* A discussion of how a hierarchical organization of the data can be used explicitly in this framework would be helpful. 
limitations_and_societal_impact: Limitations: yes

Societal impact: does not apply
review_title: null

final_score: rating: 4: Ok but not good enough - rejection
review_content: summary: This paper proposes a  VAE architecture with a hyperbolic latent space called 3D hyperbolic VAE. The proposed method is evaluated on synthetic datasets and a medical dataset, showing its effectiveness for the unsupervised 3D segmentation task.
main_review: **Originality:**  The novelty is limited, as the proposed method is a combination of exsiting techniques. However, I appreciate that this paper tries to solve a difficult task, i.e., unsupervised segmentation of 3D voxel-grid biomedical images.

**Quality:**  (1) I would suggest authors to use more evaluation criterions. In the 3D biomedical segmentation task, Jaccord score,  95% Hausdorff Distance (95HD), and  Average Surface Distance (ASD) are  also usually used. Providing results in terms of these criterions can better evaluate the proposed method.

(2) Table 3 shows the comparision with previous state-of-the-art methods on BraTS 2019. But there are not many methods on the unsupervised segmentation task. I think authors should consider more methods for comparision, even some frameworks in the computer vision field for the unsupervised segmentation of 3D data (e.g., videos). Plus, more real medical datasets can be used for evaluation.

**Clarity:**  The paper is clearly written and easy to follow.

Overall, I am concern about the novelty of the paper, and authors may need to provide more experimental results in terms of different evaluation indicators and more real datasets. So, I vote for rejection, but the score might be increased based on the authors' response and the discussion.
limitations_and_societal_impact: There is no evident limitation in the proposed method.
review_title: null

Meta-review: This paper addresses the unsupervised segmentation of 3D biomedical images. The authors propose to leverage hyperbolic representations as continuous models for modeling hierarchical structures. A VAE based on Gyroplane convolutional layers at the decoder level is introduced, and trained with a self-supervised loss in addition to the ELBO data fitting based on making distance between .

The paper initially received three weak accept and one weak reject recommendation. The main concerns pointed out by reviewers were about the lack of novelty of the proposed method, and the need to compare to stronger baselines and on broader datasets. After rebuttal, all reviewers stick on their initial ratings. 

The AC carefully read the submission and authors' feedback. Although the approach uses known tools (hyperbolic representation, self-supervised learning), the AC considers that its adaptation for unsupervised 3D biomedical image segmentation is meaningful. The paper is also well written and clear. The experiments are overall convincing, although the AC agrees with the reviewers' concerns related to the fact that the synthetic dataset could have been designed more thoroughly. 
The AC thus recommends acceptance, but highly encourages the authors to carefully take into account and reviewers' comments and there feedback to improve the final version of the paper. Especially, the different metrics should be included in the main paper, and the author should also verify that their reimplementation of the baselines reach the performances reported in the published papers. The submission has been discussed with the senior area chair who agrees with the recommendation.
Decision: Accept (Poster)