  Witnessed the development of deep learning in recent years, increasing number
of researches try to adopt deep learning model for medical image analysis.
However, the usage of deep learning networks for the pathological image
analysis encounters several challenges, e.g. high resolution (gigapixel) of
pathological images and lack of annotations of cancer areas. To address the
challenges, we proposed a complete framework for the pathological image
classification, which consists of a novel training strategy, namely reversed
active learning (RAL), and an advanced network, namely atrous DenseNet (ADN).
The proposed RAL can remove the mislabel patches in the training set. The
refined training set can then be used to train widely used deep learning
networks, e.g. VGG-16, ResNets, etc. A novel deep learning network, i.e. atrous
DenseNet (ADN), is also proposed for the classification of pathological images.
The proposed ADN achieves multi-scale feature extraction by integrating the
atrous convolutions to the Dense Block. The proposed RAL and ADN have been
evaluated on two pathological datasets, i.e. BACH and CCG. The experimental
results demonstrate the excellent performance of the proposed ADN + RAL
framework, i.e. the average patch-level ACAs of 94.10% and 92.05% on BACH and
CCG validation sets were achieved.
