Inherently Interpretable Time Series Classification via Multiple Instance Learning

通过多实例学习实现内在可解释性时序分类.

Loss-based Attention for Deep Multiple Instance Learning

深度多实例学习中的基于损失的注意力.

Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images

基于全切片病理图像的干预式包多实例学习.

scMoE: single-cell Multi-Modal Multi-Task Learning via Sparse Mixture-of-Experts

scMoE：使用稀疏混合专家进行单细胞多模态多任务学习.

Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology

Additive MIL：用于病理学的本质可解释多实例学习.

TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

TransMIL：基于Transformer的相关多实例学习用于全切片图像分类.