Multiple instance learning (MIL) has been successfully applied for whole slide images (WSIs) analysis in computational pathology, enabling a wide range of prediction tasks from tumor subtyping to inferring genetic mutations and multi-omics biomarkers. However, existing MIL methods predominantly focus on single-task learning, resulting in not only overall low efficiency but also the overlook of inter-task relatedness. To address these issues, we proposed an adapted architecture of Multi-gate Mixture-of-experts with Multi-proxy for Multiple instance learning (M4), and applied this framework for simultaneous prediction of multiple genetic mutations from WSIs. The proposed M4 model has two main innovations: (1) utilizing a mixture of experts with multiple gating strategies for multi-genetic mutation prediction on a single pathological slide; (2) constructing multi-proxy expert network and gate network for comprehensive and effective modeling of pathological image information. Our model achieved significant improvements across five tested TCGA datasets in comparison to current state-of-the-art single-task methods. The code is available at:https://github.com/Bigyehahaha/M4.

翻译：多示例学习（MIL）已成功应用于计算病理学中的全切片图像（WSI）分析，支持从肿瘤分型到推断基因突变及多组学生物标志物在内的广泛预测任务。然而，现有MIL方法主要集中于单任务学习，这不仅导致整体效率低下，也忽视了任务间的关联性。为解决这些问题，我们提出了一种适用于多示例学习的多代理多门混合专家改进架构（M4），并将该框架应用于从WSI同时预测多种基因突变。所提出的M4模型具有两大创新点：（1）采用具有多门控策略的混合专家模型，实现对单张病理切片的多基因突变预测；（2）构建多代理专家网络与门控网络，以全面有效地建模病理图像信息。相较于当前最先进的单任务方法，我们的模型在五个测试TCGA数据集上均取得了显著性能提升。代码发布于：https://github.com/Bigyehahaha/M4。