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 whole-slide-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.

提出了一种在弱监督学习下用于疾病定位的方法，使用预训练的深度卷积网络、特征嵌入、以及基于多实例学习的顶部实例和负面证据学习等技术，即使在没有像素级别注释的情况下，仅通过图像级标签进行训练也能够展现出与强监督学习相当的性能。