Analysis of histopathology slides is a critical step for many diagnoses, and in particular in oncology where it defines the gold standard. In the case of digital histopathological analysis, highly trained pathologists must review vast wholeslide-images of extreme digital resolution (100, 000 2 pixels) across multiple zoom levels in order to locate abnormal regions of cells, or in some cases single cells, out of millions. The application of deep learning to this problem is hampered not only by small sample sizes, as typical datasets contain only a few hundred samples, but also by the generation of ground-truth localized annotations for training interpretable classification and segmentation models. We propose a method for disease localization in the context of weakly supervised learning, where only image-level labels are available during training. Even without pixel-level annotations, we are able to demonstrate performance comparable with models trained with strong annotations on the Camelyon-16 lymph node metastases detection challenge. We accomplish this through the use of pre-trained deep convolutional networks, feature embedding, as well as learning via top instances and negative evidence, a multiple instance learning technique from the field of semantic segmentation and object detection.
The authors approach the task of labeling histology images with just a single global label, with promising results on two different data sets. This is of high relevance given the difficulty in obtaining expert annotated data. At the same time the key elements of the presented approach remain identical to those in a previous study, the main novelty is to replace the final step of the previous architecture (that averages across a vector) with a multiplayer perceptron.  As such I feel that this would be interesting to present if there is interest in the overall application (and results of the 2016 CVPR paper), but not necessarily as a novel contribution to MIL and histology image classification.
Comments to the authors:
* The intro starts from a very high clinical level. A introduction that points out specifics of the technical aspects of this application, the remaining technical challenges, and the contribution of this work might be appreciated by some of your readers.
* There is preprocessing that includes feature extraction, and part of the algorithm that includes the same feature extraction. This is somewhat confusing to me and maybe you want to review the structure of the sections.  You are telling us you are using the first layer (P=1) of the ResNet50 in the method description, and you mention that you are using the pre-final layer in the preprocessing section. I assume you are using the latter, or is P=1 identical to the prefinal layer in your notation?  Tell us. Moreover, not having read Durand 2016, I would appreciate a few more technical details or formal description here and there.  Can you detail about the ranking method in Durand 2016, for example?
* Would it make sense to discuss Durand 2016 in the base line methods section? 
* To some degree this paper evaluates WELDON (Durand 2016) on new data, and compares it against and an extended WELDON algorithm called CHOWDER that features the final MLP step. Results in table 1 suggest that this leads to some 2-5% performance increase which is a nice result.  I would assume that experimental conditions (training data, preprocessing, optimization, size of ensemble) are kept constant in between those two comparisons? Or is there anything of relevance that also changed (like size of the ensemble, size of training data) because the WELDON results are essentially previously generated results? Please comment in case there are differences.