  Histopathology image analysis can be considered as a Multiple instance
learning (MIL) problem, where the whole slide histopathology image (WSI) is
regarded as a bag of instances (i.e, patches) and the task is to predict a
single class label to the WSI. However, in many real-life applications such as
computational pathology, discovering the key instances that trigger the bag
label is of great interest because it provides reasons for the decision made by
the system. In this paper, we propose a deep convolutional neural network (CNN)
model that addresses the primary task of a bag classification on a WSI and also
learns to identify the response of each instance to provide interpretable
results to the final prediction. We incorporate the attention mechanism into
the proposed model to operate the transformation of instances and learn
attention weights to allow us to find key patches. To perform a balanced
training, we introduce adaptive weighing in each training bag to explicitly
adjust the weight distribution in order to concentrate more on the contribution
of hard samples. Based on the learned attention weights, we further develop a
solution to boost the classification performance by generating the bags with
hard negative instances. We conduct extensive experiments on colon and breast
cancer histopathology data and show that our framework achieves
state-of-the-art performance.
