Venue: MIDL 2018 Conference

Reviews:

final_score: rating: 4: Top 50% of accepted papers, clear accept
review_content: The paper is in general well written, and even though section 2.1 can be considered a challenge to be explained, its figures make the reader build intuition about it. The experimental section looks very convincing.
The choice of the dataset fits quite well the assumptions made by the paper, but in general, it would be interesting to have some discussion as to when this kind of approach could be applied. Submitting this paper to this conference is also germane given the amount of 3d scans in the field (with the caveat that invariance to all 3d transformations may not apply), so I recommend to accept this paper.
review_title: On this paper the authors extend G-CNNs to the group of transformations consisting in rotations and reflections in 3D, and apply this to lung nodule detection on 3D CT scans.

final_score: rating: 4: Top 50% of accepted papers, clear accept
review_content: The study evaluates CNNs with a different type of convolutional filters. The idea is intuitive and results illustrate an impressive speed up in training time. Clinical application is nodule detection.

Pro: This is an interesting study, featuring a CNN modification that will be of interest across application domains and for many attendees of the conference.

Con: Lung nodules have symmetric properties that may be a particularly good match for the type of network modification described here. If accepted, maybe the authors will comment?
review_title: intuitive idea, good results

final_score: rating: 3: Marginally above acceptance threshold
review_content: While the motivation of including additional transformations (e.g., rotations and reflections) of convolutional kernels is easy to follow, it is not clear if the limited transformations considered in this paper could largely cover real situations.

It is interesting to see if the general CNN could have similar performance compared with G-CNN by including simple data augmentation (e.g., random rotation & flipping) operations during training, especially in the case of small training data (e.g., 30 & 300).

Based on Table 2, we could observe that D_{4h} performed even worse than D_4 in the case of limited training samples (e.g., 30 & 300). This partially contradicts with the authors’ assumption, since D_{4h} should be more powerful than D_4 in dealing with small training data due to more complicated transformations were considered.

The authors present four variants of their G-CNN, while it is hard to determine which one should be considered or selected from the practical application view.

review_title: The authors propose an interesting extension of 3D CNN by replacing general convolutional kernels with groups of convolutions, in which multiple transformations (rotation & flip) of each general kernel is considered to enhance the degree of weight sharing. The proposed group convolutional neuro networks (G-CNNs) are then applied to detecting pulmonary nodule in CT scans, showing competitive performance compared with general CNN, especially when the training data is limited.

Decision: Oral