DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification (2407.03575v1)

Abstract: 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}.

• The paper introduces DGR-MIL, a novel framework that leverages diverse global vectors to capture instance heterogeneity in MIL for WSI classification.
• It employs a cross-attention mechanism with DPP-based diversification to align positive instances and mitigate redundancy.
• Experimental results on CAMELYON-16 and TCGA-NSCLC datasets demonstrate significant improvements in accuracy, F1 score, and AUC.

Exploration of Diverse Global Representations in MIL for WSI Classification

The paper "DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification" explores the use of a novel diverse global representation framework to enhance the performance of Multiple Instance Learning (MIL) models in classifying histological whole slide images (WSIs). Traditional MIL approaches often fail to account for intrinsic instance diversity, focusing instead on instance correlation. This paper introduces a new method, DGR-MIL, which models diversity through a set of global vectors.

The concept of MIL is crucial in the context of WSI analysis where high-resolution images are segmented into smaller patches, each labeled as either a positive or negative instance, forming a 'bag'. While current techniques target instance correlation, they largely overlook within-bag and between-bag heterogeneity. The proposed DGR-MIL attempts to capture this by utilizing a cross-attention mechanism that leverages predefined global vectors to interpret diverse instances effectively.

Key innovations of the proposed framework include positive instance alignment and a unique diversification learning paradigm based on the determinantal point process (DPP). Positive instance alignment encourages the global vectors to converge towards the exemplary characteristics of positive instances (tumor regions), thereby enhancing the discriminative power of the MIL model. Meanwhile, the DPP-driven method enforces orthogonality among the global vectors, allowing them to encompass distinct instance characteristics and mitigate redundancy effectively.

The methodological advancements presented in this research demonstrate notable improvements over existing MIL models when tested on datasets like CAMELYON-16 and TCGA-NSCLC. The proposed DGR-MIL setup achieves a significant leap in accuracy, F1 score, and AUC, underscoring its efficacy in tasks involving complex, diverse medical image datasets.

Results on the CAMELYON-16 dataset, for instance, show that DGR-MIL achieves an accuracy surpassing existing methods, addressing one of the main criticisms of current MIL approaches—handling of instance diversity. The rigorous methodological design also underscores the feasibility of implementation in computational pathology, aiding cancer diagnosis and prognosis. Furthermore, by capturing WSI intricacies, DGR-MIL paves the way for MIL models capable of tackling broader challenges in medical image analysis.

In summary, this work proposes a paradigm shift in MIL for WSI classification, moving from merely focusing on instance correlation to a nuanced approach recognizing and leveraging instance diversity. The implications are profound, particularly in enhancing model robustness and interpretability, thereby improving clinical decision-support systems. The paper suggests future exploration in MIL should extend to other domains, leveraging contextual global representations to address complex datasets across various fields in artificial intelligence.