Abstract

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Title:: ABMIL-BiGRU: bidirectional gated recurrent unit attention based multi-instance learning for interpretable prediction of sentinel lymph node metastasis in breast cancer

Author(s):: LI Bo1; YANG Yanbin2; LI Shuai3; LIANG Meiyan3; 1. Department of Physical Therapy, Shanxi Rongjun Hospital, Taiyuan 030031, China 2. Department of Rehabilitation, Shanxi Civil Administration Rehabilitation Hospital, Taiyuan 030032, China 3. College of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China

Keywords:: Keywords: breast cancer lymph node metastasis precise diagnosis bidirectional gated recurrent unit contextual information interpretability whole slide image

Abstract:: Abstract: Aiming at the classification and lesion localization of giga-pixel pathology whole slide images of breast cancer, a bidirectional gated recurrent unit attention based multi-instance learning (ABMIL-BiGRU) model is proposed for interpretable prediction of H&E stained breast cancer lymph node metastasis images. The method uses two orthogonal bidirectional gated recurrent units to establish the long-short distance dependencies between the features in the row and column directions of the image block, thereby embedding the spatial position and context information of the image block, and then quantifies the attention score of each feature representation through attention multi-instance pooling, thereby achieving whole slide image-level feature aggregation and generating interpretable heat maps. The results show that ABMIL-BiGRU model has an average accuracy of 0.918 6 and an AUC?f 0.946 7 on the breast cancer metastasis dataset, realizing high-precision prediction of whole slide images and localization of regions of interest, and also providing human-interpretable features at the image block level. The proposed model solves the "accuracy-interpretability trade-off" problem to a certain extent, and its superior performance provides a new paradigm for the clinical application of computer-aided diagnosis and intelligent systems.