Venue: MIDL 2018 Conference

Reviews:

final_score: rating: 2: Marginally below acceptance threshold
review_content: 
Quality & Clarity

#1. The organization of this paper is not good and need to be re-organized.
#2. The result section of this paper should be clarified.

Originality & Significance

(+) The problem definition was interesting.
(-) There is no technical novelty.
(-) There is no baseline method. Authors should compare to other methods.
(-) The details for model training is not included in this paper.
(-) Tuning section is only general data augmentation scheme.
review_title: This paper presents cervical cytology diagnosis using object detection framework, Faster R-CNN. The authors have applied the Faster R-CNN to new cervical data and did not make any performance comparison to the other baseline methods.

final_score: rating: 2: Marginally below acceptance threshold
review_content: The paper proposes to use Faster RCNN for cervical cytology diagnosis.

pros
+ relevant problem, relevant architecture

cons
- weak motivation for the model of choice, limited experimental evaluation, clarity could be improved

*The paper presentation could be significantly improved. 
*The choice of Faster RCNN should be properly motivated, why did you choose this model instead of other more recent approaches such as YOLO or MaskRCNN?
*Data preparation: Section 2 states that 500 samples are gathered, 450 positive cases and 50 negative cases. Then,50 positive and 50 negative samples are selected from these 500 as test set. Does this mean that the training set only has positive samples?
*What the influence on the 0.9 threshold to extract foreground and background?
*It would be appropriate to include a more detailed review of Faster RCNN.
*RPN is not introduced in the text.
*Why only report accuracy on the test set and not other metrics reported in validation? (page 5)
*Table 3: having 3 columns for TP, FP and FN would be beneficial (instead of TP, TP+FP, TP+FN).
*The method is not compared to any baseline, making it hard to assess the contribution.
*Is data augmentation applied during training?
*The so-called data enhancement strategy has been widely used in the literature, please reference some works.
*Why is the overlapping threshold of the data enhancement part set to 0.6?
*Do results on table 3 correspond to data enhancement?
*Tables 4 and 5 could be merged.
*The authors reference literature on the same topic using deep learning, it would be important to provide results of these other methods as baselines.
*If experiments were performed using Inception V3 as well, it would be good to report results.
*The discussion claims that ASC-US, LSIL and HSIL are referred to as positive cells. Why do the authors think the taking into account independent classes leads to low performance? How could this be improved?
review_title: relevant problem, relevant architecture, limited experimental evaluation

final_score: rating: 1: Strong rejection
review_content: The work explores the use of faster R-CNN for automatic detection in cervical cytology.

Although this is a good study applying a recent deep learning method to a new application, I am not sure this is a good fit to the MIDL conference. The pipeline including pre-processing is very specific to the particular data, and since the methodology itself is not novel, it is a bit unclear what to learn from the paper.

I believe it would be a better fit to a more application focused venue or journal.



review_title: new application of faster R-CNN

Decision: Reject