Data-driven Feature Sampling for Deep Hyperspectral Classification and Segmentation
The high dimensionality of hyperspectral imaging forces unique challenges in scope, size and processing requirements. Motivated by the potential for an in-the-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.
 In this paper, the authors proposed a framework to classify cells and implement cell segmentation based on the deep learning techniques. Using the classification results to guide the feature selection method, the method can achieve comparable performance even 90% of the input features are reduced. In general, the paper addresses an interesting problem, but the technical contribution is rather incremental. The authors seem to apply some well-define methods to realize a new task. The authors are expected to clarify their technical contributions or model improvement to address the specific problem. Moreover, there also exist some recent progress on image segmentation, such as FCN or mask R-CNN. The authors are expected to demonstrate the results by improving these advanced models. In general, this is an interesting paper, but would be more fit to MICCAI or ISBI.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Data-driven Feature Sampling for Deep Hyperspectral Classification and Segmentation
The high dimensionality of hyperspectral imaging forces unique challenges in scope, size and processing requirements. Motivated by the potential for an in-the-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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 In this paper, the authors proposed a framework to classify cells and implement cell segmentation based on the deep learning techniques. Using the classification results to guide the feature selection method, the method can achieve comparable performance even 90% of the input features are reduced. In general, the paper addresses an interesting problem, but the technical contribution is rather incremental. The authors seem to apply some well-define methods to realize a new task. The authors are expected to clarify their technical contributions or model improvement to address the specific problem. Moreover, there also exist some recent progress on image segmentation, such as FCN or mask R-CNN. The authors are expected to demonstrate the results by improving these advanced models. In general, this is an interesting paper, but would be more fit to MICCAI or ISBI.
assistant
 In this paper, the authors proposed a framework to classify cells and implement cell segmentation based on the deep learning techniques. Using the classification results to guide the feature selection method, the method can achieve comparable performance even 90% of the input features are reduced. In general, the paper addresses an interesting problem, but the technical contribution is rather incremental. The authors seem to apply some well-define methods to realize a new task. The authors are expected to clarify their technical contributions or model improvement to address the specific problem. Moreover, there also exist some recent progress on image segmentation, such as FCN or mask R-CNN. The authors are expected to demonstrate the results by improving these advanced models. In general, this is an interesting paper, but would be more fit to MICCAI or ISBI.