Second-order multi-instance learning model for whole slide image classification

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 105 × 105pixels). 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. Additionally, we utilize an efficient channel attention mechanism to adaptively highlight the most discriminative instance features, followed by an RNN to update the final bag-level representation for the slide classification. Experimental results on the lymph node metastasis dataset of 2016 Camelyon grand challenge demonstrate the significant improvement of our proposed SoMIL framework compared with other state-of-the-art multi-instance learning methods. Moreover, in the external validation on 130 WSIs, SoMIL also achieves an impressive area under the curve performance that competitive to the fully-supervised framework.