Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification

• URL: http://arxiv.org/abs/2208.03526v1
• Date: Sat, 6 Aug 2022 14:36:48 GMT
• Title: Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification
• Authors: Zhikang Wang, Yue Bi, Tong Pan, Chris Bain, Richard Bassed, Seiya Imoto, Jianhua Yao, Jiangning Song
• Abstract summary: Multiple instance learning (MIL) is a powerful approach to classify whole slide images (WSIs) for diagnostic pathology. We propose a novel multiplex-detection-based multiple instance learning (MDMIL) to tackle the issues above. Specifically, MDMIL is constructed by the internal query generation module (IQGM) and the multiplex detection module (MDM)
• Score: 2.61155594652503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple instance learning (MIL) is a powerful approach to classify whole slide images (WSIs) for diagnostic pathology. A fundamental challenge of MIL on WSI classification is to discover the \textit{critical instances} that trigger the bag label. However, previous methods are primarily designed under the independent and identical distribution hypothesis (\textit{i.i.d}), ignoring either the correlations between instances or heterogeneity of tumours. In this paper, we propose a novel multiplex-detection-based multiple instance learning (MDMIL) to tackle the issues above. Specifically, MDMIL is constructed by the internal query generation module (IQGM) and the multiplex detection module (MDM) and assisted by the memory-based contrastive loss during training. Firstly, IQGM gives the probability of instances and generates the internal query (IQ) for the subsequent MDM by aggregating highly reliable features after the distribution analysis. Secondly, the multiplex-detection cross-attention (MDCA) and multi-head self-attention (MHSA) in MDM cooperate to generate the final representations for the WSI. In this process, the IQ and trainable variational query (VQ) successfully build up the connections between instances and significantly improve the model's robustness toward heterogeneous tumours. At last, to further enforce constraints in the feature space and stabilize the training process, we adopt a memory-based contrastive loss, which is practicable for WSI classification even with a single sample as input in each iteration. We conduct experiments on three computational pathology datasets, e.g., CAMELYON16, TCGA-NSCLC, and TCGA-RCC datasets. The superior accuracy and AUC demonstrate the superiority of our proposed MDMIL over other state-of-the-art methods.

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