  Multiple instance learning (MIL) stands as a powerful approach in weakly
supervised learning, regularly employed in histological whole slide image (WSI)
classification for detecting tumorous lesions. However, existing mainstream MIL
methods focus on modeling correlation between instances while overlooking the
inherent diversity among instances. However, few MIL methods have aimed at
diversity modeling, which empirically show inferior performance but with a high
computational cost. To bridge this gap, we propose a novel MIL aggregation
method based on diverse global representation (DGR-MIL), by modeling diversity
among instances through a set of global vectors that serve as a summary of all
instances. First, we turn the instance correlation into the similarity between
instance embeddings and the predefined global vectors through a cross-attention
mechanism. This stems from the fact that similar instance embeddings typically
would result in a higher correlation with a certain global vector. Second, we
propose two mechanisms to enforce the diversity among the global vectors to be
more descriptive of the entire bag: (i) positive instance alignment and (ii) a
novel, efficient, and theoretically guaranteed diversification learning
paradigm. Specifically, the positive instance alignment module encourages the
global vectors to align with the center of positive instances (e.g., instances
containing tumors in WSI). To further diversify the global representations, we
propose a novel diversification learning paradigm leveraging the determinantal
point process. The proposed model outperforms the state-of-the-art MIL
aggregation models by a substantial margin on the CAMELYON-16 and the TCGA-lung
cancer datasets. The code is available at
\url{https://github.com/ChongQingNoSubway/DGR-MIL}.
