# DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification

arXiv: https://arxiv.org/abs/2407.03575 · HTML: https://mlsanity.com/paper/2407.03575/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 65 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Linformer: Self-Attention with Linear Complexity](https://mlsanity.com/paper/2006.04768/) (2020)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [Nystr"omformer: A Nystr"om-Based Algorithm for Approximating Self-Attention](https://mlsanity.com/paper/2102.03902/) (2021)
- [Efficient Attention: Attention with Linear Complexities](https://mlsanity.com/paper/1812.01243/) (2018)
- [Lookahead Optimizer: k steps forward, 1 step back](https://mlsanity.com/paper/1907.08610/) (2019)
- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification](https://mlsanity.com/paper/1504.07947/) (2015)
- [Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction](https://mlsanity.com/paper/2006.08558/) (2020)
- [Revisiting Multiple Instance Neural Networks](https://mlsanity.com/paper/1610.02501/) (2016)
- [Randomized Positional Encodings Boost Length Generalization of Transformers](https://mlsanity.com/paper/2305.16843/) (2023)
- [Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks](https://mlsanity.com/paper/2105.02358/) (2021)
- [Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images](https://mlsanity.com/paper/2303.06873/) (2023)
- [On the Computational Power of Transformers and its Implications in Sequence Modeling](https://mlsanity.com/paper/2006.09286/) (2020)
- [CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation](https://mlsanity.com/paper/1908.10555/) (2019)
- [Determinantal Point Processes in Randomized Numerical Linear Algebra](https://mlsanity.com/paper/2005.03185/) (2020)
- [Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding](https://mlsanity.com/paper/1910.10825/) (2019)
- [Inherently Interpretable Time Series Classification via Multiple Instance Learning](https://mlsanity.com/paper/2311.10049/) (2023)
- [Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning](https://mlsanity.com/paper/2210.09452/) (2022)

## Built on by (most cited first, 25 of 25)

- [How Effective Can Dropout Be in Multiple Instance Learning ?](https://mlsanity.com/paper/2504.14783/) (2025)
- [M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple Instance Learning in Histopathology Image Analysis](https://mlsanity.com/paper/2407.17267/) (2024)
- [Act Like a Pathologist: Tissue-Aware Whole Slide Image Reasoning](https://mlsanity.com/paper/2603.00667/) (2026)
- [RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency](https://mlsanity.com/paper/2411.15076/) (2024)
- [Momentum Memory for Knowledge Distillation in Computational Pathology](https://mlsanity.com/paper/2602.21395/) (2026)
- [PathVQ: Reforming Computational Pathology Foundation Model for Whole Slide Image Analysis via Vector Quantization](https://mlsanity.com/paper/2503.06482/) (2025)
- [Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis](https://mlsanity.com/paper/2507.08178/) (2025)
- [PTCMIL: Multiple Instance Learning via Prompt Token Clustering for Whole Slide Image Analysis](https://mlsanity.com/paper/2507.18848/) (2025)
- [Continual Multiple Instance Learning with Enhanced Localization for Histopathological Whole Slide Image Analysis](https://mlsanity.com/paper/2507.02395/) (2025)
- [SGPMIL: Sparse Gaussian Process Multiple Instance Learning](https://mlsanity.com/paper/2507.08711/) (2025)
- [SMILE: a Scale-aware Multiple Instance Learning Method for Multicenter STAS Lung Cancer Histopathology Diagnosis](https://mlsanity.com/paper/2503.13799/) (2025)
- [Exploiting Low-Dimensional Manifold of Features for Few-Shot Whole Slide Image Classification](https://mlsanity.com/paper/2505.15504/) (2025)
- [Exploiting Label-Independent Regularization from Spatial Dependencies for Whole Slide Image Analysis](https://mlsanity.com/paper/2602.19487/) (2026)
- [DLiPath: A Benchmark for the Comprehensive Assessment of Donor Liver Based on Histopathological Image Dataset](https://mlsanity.com/paper/2506.03185/) (2025)
- [Contrastive Cross-Bag Augmentation for Multiple Instance Learning-based Whole Slide Image Classification](https://mlsanity.com/paper/2508.03081/) (2025)
- [Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping](https://mlsanity.com/paper/2508.15904/) (2025)
- [MICACL: Multi-Instance Category-Aware Contrastive Learning for Long-Tailed Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2509.04344/) (2025)
- [EfficientMIL: Efficient Linear-Complexity MIL Method for WSI Classification](https://mlsanity.com/paper/2509.23640/) (2025)
- [Fourier Transform Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2510.15138/) (2025)
- [Positive Semi-definite Latent Factor Grouping-Boosted Cluster-reasoning Instance Disentangled Learning for WSI Representation](https://mlsanity.com/paper/2511.01304/) (2025)
- [CAPRMIL: Context-Aware Patch Representations for Multiple Instance Learning](https://mlsanity.com/paper/2512.14540/) (2025)
- [Prototype Instance-semantic Disentanglement with Low-rank Regularized Subspace Clustering for WSIs Explainable Recognition](https://mlsanity.com/paper/2602.14501/) (2026)
- [MacNet: An End-to-End Manifold-Constrained Adaptive Clustering Network for Interpretable Whole Slide Image Classification](https://mlsanity.com/paper/2602.14509/) (2026)
- [Diffusion Attention Expert Model for Predicting and Semi-automatic Localizing STAS in Lung Cancer Histopathological Images](https://mlsanity.com/paper/2605.16444/) (2026)
- [A Multi-Cohort Validation of Censoring-Aware Conformal Lower Predictive Bounds for Pathology Survival Models](https://mlsanity.com/paper/2608.04025/) (2026)

## Abstract (arXiv)

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 urlhttps://github.com/ChongQingNoSubway/DGR-MIL.