  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.
