Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 6: Weak Accept
review_content: - The authors proposed a novel method for cryo-EM reconstruction that extends naturally to modeling continuous generative factors of structural heterogeneity. To address intrinsic protein structural heterogeneity, they explicitly model the imaging operation to disentangle the orientation of the molecule by formulating decoder as a function of Cartesian coordinates.

- The problem and the approach are well motivated. 

- This reviewer has the following comments:
1) VAE is known to generate blurred images. Thus, based on this approach, the reconstruction image may not be optimal with respect to the resolution which might be critical for cryo-EM reconstruction. What's your opinion?
2) What's the relationship between reconstructed performance, heterogeneity of the sample and dimensions of latent space?
3) It would be interesting to show any relationship, reconstruction error with respect to the number of discrete multiclass.  
4) How is the proposed method generalizable?
review_title: Official Blind Review #1

final_score: rating: 8: Accept
review_content: ~The authors build a new method to recapitulate the 3D structure of a biomolecule from cryo-EM images that allows for flexibility in the reconstructed volume.~

I thought this paper is very well written and tackles a difficult project. 

There is a previous work that these authors should cite:

Ullrich, K., Berg, R.V.D., Brubaker, M., Fleet, D. and Welling, M., 2019. Differentiable probabilistic models of scientific imaging with the Fourier slice theorem. arXiv preprint arXiv:1906.07582.

How does your method compare to this paper? In Ullrich et al., they report “Time until convergence, MSE [10^-3/voxel], and Resolution [Angstrom]). I think these statistics would be useful to report in your work, as they are more familiar with folks in the cryoEM field.

In Equation 3, how does one calculate Z, the normalization constant?

For the decoder, how large of the 3D space are you generating? What are the units? Are you using voxels to represent atomic density? What is the voxel size? Is it the same as on Page 11?

I think more description of the neural network architecture would be useful (more than what is reported on page 12).

review_title: Official Blind Review #3

final_score: rating: 8: Accept
review_content: The authors introduce cryoDRGN, a VAE neural network architecture to reconstruct 3D protein structure from 2D cryo-EM images. 

The paper offers for a good read and diagrams are informative.

Below are comments for improvement and clarification.

> Consider explaining cryoSPARC in detail given that is the state-of-the-art technique and to which all the cryoDGRN results are compared.

> In Figure 4 and the related experiment,  how are a) the cryoSPARK volumes related to cryoDRGN volumes, b) what do the clusters mean in cryoSPARK and how do they compare with the corresponding outputs of cryoDRGN

> What would runtime comparisons be for cryoSPARK and cryoDGRN, for an unsupervised heteregeneous reconstruction?
review_title: Official Blind Review #2

Meta-review: The paper introduces a generative approach to reconstruct 3D images for cryo-electron microscopy (cryo-EM).

All reviewers really liked the paper, appreciate the challenging problem tackled and the proposed solution.

Acceptance is therefore recommended. 
Decision: Accept (Spotlight)