Paper:
The high dimensionality of hyperspectral imaging forces unique challenges in scope, size and processing requirements. Motivated by the potential for an inthe-field cell sorting detector, we examine a Synechocystis sp. PCC 6803 dataset wherein cells are grown alternatively in nitrogen rich or deplete cultures. We use deep learning techniques to both successfully classify cells and generate a mask segmenting the cells/condition from the background. Further, we use the classification accuracy to guide a data-driven, iterative feature selection method, allowing the design neural networks requiring 90% fewer input features with little accuracy degradation.

Review:
This paper explores the use of neural networks for classification and segmentation of hypersepctral imaging (HSI) of cells. The basic set-up of the method and results seem correct and will be useful to the specific application described. While the narrow scope might limit this work's significance, my main issue with the paper is that while the authors describe prior art in terms of HSI for biological preps, there is a very rich literature addressing HSI images in other domains: in particular for remote sensing. I think that this work can (1) be a lot clearer as to the novelty and (2) have a much bigger impact if this literature is addressed. In particular, there are a number of methods of supervised and unsupervised feature extraction used for classification purposes (e.g. endmember extraction or dictionary learning). It would be great to know how the features extracted from the neural network compare to these methods, as well as how the classification performance compares to typical methods, such as performing SVM classification in the feature space. The comparison with the random forests is nice, but that is not a standard method. Putting the presented work in context for these other methods would help make there results more general, and hopefully increase the applicability to more general HSI data (thus increasing significance).  
An additional place where this comparison to the larger HSI literature would be useful is in the section where the authors describe the use of the network weights to isolate sub-bands that are more informative than others. Given the high correlation in many spectra, typically something like random sampling might be sufficient (as in compressive sensing). This type of compression which can be applied at the sensor -- a benefit the authors mention of their band-wise sub-sampling. It would be good to acknowledge this prior work and to understand if the features from the network are superior to the random sampling scheme. 
For these comments, I suggest the authors look at the following papers (and especially the references therein):
[1] Li, Chengbo, et al. "A compressive sensing and unmixing scheme for hyperspectral data processing." IEEE Transactions on Image Processing 21.3 (2012): 1200-1210.
[2] Bioucas-Dias, José M., et al. "Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches." IEEE journal of selected topics in applied earth observations and remote sensing 5.2 (2012): 354-379.
[3] Charles, Adam S., Bruno A. Olshausen, and Christopher J. Rozell. "Learning sparse codes for hyperspectral imagery." IEEE Journal of Selected Topics in Signal Processing 5.5 (2011): 963-978.