Venue: MIDL 2018 Conference

Reviews:

final_score: rating: 4: Top 50% of accepted papers, clear accept
review_content: In this paper, the author presented a patch-based FCN network for discriminative regions retrieval. Multiple aggregation and selection methods are evaluated. The paper is generally well-written and easy to understand. The results look good.

Here are major comments:
1.	The method is called “weakly supervised” which is usually used for segmentation/localization task with image-level annotation. In contrast, this work is for pure classification task aided with coarsely annotated discriminative regions. Changing it to another name may be more precise.
2.	Why not using CNN based classification at the last stage but random forest?
3.	As mentioned coarse annotation has errors, would iteratively updating the coarse annotation using the prediction/localization result help the overall performance?
4.	Would focal loss help the accuracy of SCLC?
5.	In equation 1, y_c or y_k?
6.	In Fig 2, 2x2x512 -> 1x1x1024, how this is done with 1x1 kernel without padding? So is the 22x22x512 -> 21x21x1024
7.	Would class balance weight in loss function help?
8.	What’s red in Figure 4? Normal?

review_title: Good paper, need some clarification to be stronger

final_score: rating: 4: Top 50% of accepted papers, clear accept
review_content: (1) The paper is well-written and easy to follow.

(2) The proposed method is technically sound and could be potentially used to assist pathologists for histology image diagnosis. 

(3) The authors claim that the proposed method can achieve fast and effective classification on whole slide lung cancer images, while there is no clue to show the “fast” property of the proposed method. The comparison of computational complexity of competing method and corresponding analysis in detail should be helpful. 

(4) The global descriptors produced by the proposed method are further fed into a RF classifier. Why not consider an end-to-end classification strategy? 

(5) Some details are missing. For example, the description in Sec. 3.1 is not clear: "the rest 812 cancer images only carry the WSI-level labels, which are further split into 642 (D2) and 170 (D4) images..." Is there any criterion to perform this kind of split? 

review_title: This paper proposes a weakly-supervised learning method to address whole slide lung cancer image classification problem with minimum annotation information. Besides, it adopts context-aware feature selection and aggregation strategy to obtain a good holistic feature representation of whole slide images (WSIs) to improve the WSI-level prediction performance. 

final_score: rating: 2: Marginally below acceptance threshold
review_content: An algorithm for histopathology classification is presented that fuses both image-level labels and coarsely annotated tumor regions (in some scans). A convolutional architecture for predicting global labels is learned. Some pre-final features are used to generate spatial probability maps using an external classifier (here: random forest). 

Pro: The applications are well presented, authors describe what they are doing and why. Experiments are extensive, results are promising and may well generalize beyond the specifics of this application.

Con: I have the impression the prior work can be extended significantly, as the approach follows - in many aspects -  a common modeling strategy with global labels for weakly supervised learning. As such, I would see the merits on this paper rather on the application itself than on the technical specifics of the learning algorithm.
review_title: nice application study, not sure about technical innovation

Decision: Oral