Venue: MIDL 2018 Conference

Reviews:

final_score: rating: 5: Top 15% of accepted papers, strong accept
review_content: The paper describes a method to create super-resolution images from low-resolution images using a generative adversarial network approach that does not require paired low-res/hi-res images.

This was a very pleasant read and very informative. The related work is relevant and adds to the paper. The methods of this paper are well described and strongly motivated. The results are presented in a thorough and objective manner, both quantitatively and qualitatively. I would like to see more of a discussion section to put this work in a larger context at the end, but the absence might be space limited.

I would encourage the authors to add some high level talking points surrounding the practical use of the method in the discussion section, depending on available space; I would not recommend cutting down on any other portion of the paper, so this could be the limiting factor. 
Some interesting talking points to this method could include:
1) Without a high-resolution analog for validation, in the real world is there a risk of the algorithm "making up" data or structures?
2) More and more algorithms are interpreting images as opposed to humans, does super-resolution have benefits to algorithmic interpretation or is it limited to human interpretation?
3) Are there any examples of super-resolution images changing a reader's interpretation or the speed of interpretation? Or are do they simply lead to a more confident read?

The fact that the paper leads to these thought provoking questions is a testament to the strength of the paper. Well done.
review_title: Very well done!

final_score: rating: 3: Marginally above acceptance threshold
review_content: The authors investigated unsupervised SR in endomicroscopy. This can exploit HR images, regardless of the domain where they are coming from, to transfer the super-resolution to the initial LR images. This property can be particularly
useful in all situations where pairs of LR/HR are not available during the training. This study looks very challenge and very important and experimental results are relatively extensive.
review_title: Review of Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy 

final_score: rating: 5: Top 15% of accepted papers, strong accept
review_content: The paper describes a method to improve image resolution in endomicroscopy using only unpaired training samples and an adversarial framework.  This is a highly relevant topic since paired samples are difficult to obtain in many applications in medical imaging and the adversarial network has been exploited to excellent effect for this purpose in the natural image domain.  Aspects of image acquisition are used to constrain the network and enforce cycle consistency.
Overall I found this paper to be extremely relevant, well-written and clearly explained.  The technique is novel and the current state of the art is well described.  Experiments are described in good detail and are thorough in their construction.  While the authors do not have a gold-standard available they make every effort to provide quantitative and qualitative evaluation of their results and demonstrate an improvement compared to the state of the art.  It would be a nice addition to test the method in a domain where both HR and LR images are available so that a true gold standard could be utilised - however I realise that this may not be easy to achieve depending on access to data.
In conclusion I am very happy to recommend this paper for acceptance.
review_title: A well written and detailed paper on a relevant topic

Decision: Oral