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Analysis of histopathology slides is a critical step for many diagnoses, andin particular in oncology where it defines the gold standard. In the case ofdigital histopathological analysis, highly trained pathologists must reviewvast whole-slide-images of extreme digital resolution ($100,000^2$ pixels)across multiple zoom levels in order to locate abnormal regions of cells, or insome cases single cells, out of millions. The application of deep learning tothis problem is hampered not only by small sample sizes, as typical datasetscontain only a few hundred samples, but also by the generation of ground-truthlocalized annotations for training interpretable classification andsegmentation models. We propose a method for disease localization in thecontext of weakly supervised learning, where only image-level labels areavailable during training. Even without pixel-level annotations, we are able todemonstrate performance comparable with models trained with strong annotationson the Camelyon-16 lymph node metastases detection challenge. We accomplishthis through the use of pre-trained deep convolutional networks, featureembedding, as well as learning via top instances and negative evidence, amultiple instance learning technique from the field of semantic segmentationand object detection.

Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach