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Whole slide histopathology images (WSIs) play a crucial role in diagnosing lymph node metastasis of breast cancer, which usually lack fine-grade annotations of tumor regions and have large resolutions (typically 10 5× 10 5 pixels). Multi-instance learning has gradually become a dominant weakly supervised learning framework for WSI classification when only slide-level labels are available. In this paper, we develop a novel second-order multiple instances learning method (SoMIL) with an adaptive aggregator stacked by the attention mechanism and recurrent neural network (RNN) for histopathological image classification. To be specific, the proposed method applies a second-order pooling module (matrix power normalization covariance) for instance-level feature extraction of weakly supervised learning framework, attempting to explore second-order statistics of deep features for histopathological images …