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

Reversed Active Learning based Atrous DenseNet for Pathological Image Classification