Venue: MIDL 2018 Conference

Reviews:

final_score: rating: 3: Marginally above acceptance threshold
review_content:  
review_title:  

final_score: rating: 2: Marginally below acceptance threshold
review_content: This paper proposes a deep neural networks-based classification method to predict lesion growth and patient survival with CT images of colorectal cancer patients. The method was well validated in the experiments. The overall writing is satisfactory with few typos. The evaluation metrics are extensive. However, technical contribution and the demonstration of saliency maps are some kind of weak. 

Pros:
1, the descriptions of the data are detailed
2, three traditional methods were compared with the proposed framework
3, the quantitative evaluations are comprehensive with many different metrics

Cons:
1, The motivation of using an auto-encoder followed by a classifier is not clarified. Why not using a network for classification directly?
2, Several typos are found in the manuscript. E.g., “provide ,,a standard set”, “Therefore….”, etc. 
3, The saliency map was demonstrated with only one example. It’s better to show results with different classes of growth/non-growth and survival/non-survival prediction.

review_title: Little methodological novelty with decent validation

final_score: rating: 3: Marginally above acceptance threshold
review_content: The paper is well-written (with some parts of the text overextended). Information is provided to fully understand the concept and the method being tested. Would be helpful to include a sketch of the system (2 time point entry). The presented method is general and can be applied to different applications making it interesting for the conference community. 

Pros:
-	The presented system shows superior results over other methods.
-	Statistical measurements and significance were carefully computed
-	High quality evaluation with unique dataset.

Cons:
-	Some parts of the text can be reduced or written shortly (e.g. metrics, discussion).
-	The description of the datasets used and the entire procedure for handling the data is overly extended. 
-	The novelty of the method is weak


Minor comments:
Section 3.1 – the quantiles equation seems to have a wrong inequality direction for the 0.9 (?).
It is not stated what the manual segmentation was needed for – would a simple bounding box work?
Equation 2 – should state "m" in the following text.
Figure 3 – text is too small.
Check reference format.


review_title: This work presents deep learning-based network for tumor growth prediction and one-year survival prediction for CRC metastatic liver tumors. Different approaches are tested and compared showing the benefit of the system.

Decision: Poster