# DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification

arXiv: https://arxiv.org/abs/2206.08861 · HTML: https://mlsanity.com/paper/2206.08861/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2103.10626/) (2021)
- [Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging](https://mlsanity.com/paper/2111.01556/) (2021)
- [Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey](https://mlsanity.com/paper/1909.03354/) (2019)
- [Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology](https://mlsanity.com/paper/1806.05083/) (2018)
- [Multiple instance dense connected convolution neural network for aerial image scene classification](https://mlsanity.com/paper/1908.08156/) (2019)
- [Multi-Instance Multi-Scale CNN for Medical Image Classification](https://mlsanity.com/paper/1907.02413/) (2019)

## Built on by (most cited first, 30 of 40)

- [Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification](https://mlsanity.com/paper/2210.03664/) (2022)
- [The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification](https://mlsanity.com/paper/2305.17891/) (2023)
- [Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis](https://mlsanity.com/paper/2208.08789/) (2022)
- [Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Need](https://mlsanity.com/paper/2307.02249/) (2023)
- [Pathology-knowledge Enhanced Multi-instance Prompt Learning for Few-shot Whole Slide Image Classification](https://mlsanity.com/paper/2407.10814/) (2024)
- [MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models](https://mlsanity.com/paper/2403.05160/) (2024)
- [Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2403.07939/) (2024)
- [FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification](https://mlsanity.com/paper/2409.19720/) (2024)
- [Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2408.09476/) (2024)
- [Multi-level Multiple Instance Learning with Transformer for Whole Slide Image Classification](https://mlsanity.com/paper/2306.05029/) (2023)
- [Snuffy: Efficient Whole Slide Image Classifier](https://mlsanity.com/paper/2408.08258/) (2024)
- [TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2305.00696/) (2023)
- [OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification](https://mlsanity.com/paper/2307.05254/) (2023)
- [Dataset Distillation for Histopathology Image Classification](https://mlsanity.com/paper/2408.09709/) (2024)
- [Enhancing Zero-Shot Brain Tumor Subtype Classification via Fine-Grained Patch-Text Alignment](https://mlsanity.com/paper/2508.01602/) (2025)
- [ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging](https://mlsanity.com/paper/2306.10535/) (2023)
- [CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained images](https://mlsanity.com/paper/2401.16131/) (2024)
- [Few-Shot Learning from Gigapixel Images via Hierarchical Vision-Language Alignment and Modeling](https://mlsanity.com/paper/2505.17982/) (2025)
- [Contrastive Integrated Gradients: A Feature Attribution-Based Method for Explaining Whole Slide Image Classification](https://mlsanity.com/paper/2511.08464/) (2025)
- [Rethinking Attention-Based Multiple Instance Learning for Whole-Slide Pathological Image Classification: An Instance Attribute Viewpoint](https://mlsanity.com/paper/2404.00351/) (2024)
- [Learning from Majority Label: A Novel Problem in Multi-class Multiple-Instance Learning](https://mlsanity.com/paper/2509.04023/) (2025)
- [ME-Mamba: Multi-Expert Mamba with Efficient Knowledge Capture and Fusion for Multimodal Survival Analysis](https://mlsanity.com/paper/2509.16900/) (2025)
- [Multi-Scale Heterogeneity-Aware Hypergraph Representation for Histopathology Whole Slide Images](https://mlsanity.com/paper/2404.19334/) (2024)
- [MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classification](https://mlsanity.com/paper/2503.12401/) (2025)
- [GMAT: Grounded Multi-Agent Clinical Description Generation for Text Encoder in Vision-Language MIL for Whole Slide Image Classification](https://mlsanity.com/paper/2508.01293/) (2025)
- [MAPLE: Multi-scale Attribute-enhanced Prompt Learning for Few-shot Whole Slide Image Classification](https://mlsanity.com/paper/2509.25863/) (2025)
- [PreMix: Label-Efficient Multiple Instance Learning via Non-Contrastive Pre-training and Feature Mixing](https://mlsanity.com/paper/2408.01162/) (2024)
- [Efficient Classification of Histopathology Images](https://mlsanity.com/paper/2409.13720/) (2024)
- [MsaMIL-Net: An End-to-End Multi-Scale Aware Multiple Instance Learning Network for Efficient Whole Slide Image Classification](https://mlsanity.com/paper/2503.08581/) (2025)
- [Weakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervision](https://mlsanity.com/paper/2504.12132/) (2025)

## Abstract (arXiv)

Multiple Instance Learning (MIL) is widely used in analyzing histopathological Whole Slide Images (WSIs). However, existing MIL methods do not explicitly model the data distribution, and instead they only learn a bag-level or instance-level decision boundary discriminatively by training a classifier. In this paper, we propose DGMIL: a feature distribution guided deep MIL framework for WSI classification and positive patch localization. Instead of designing complex discriminative network architectures, we reveal that the inherent feature distribution of histopathological image data can serve as a very effective guide for instance classification. We propose a cluster-conditioned feature distribution modeling method and a pseudo label-based iterative feature space refinement strategy so that in the final feature space the positive and negative instances can be easily separated. Experiments on the CAMELYON16 dataset and the TCGA Lung Cancer dataset show that our method achieves new SOTA for both global classification and positive patch localization tasks.