# Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging

arXiv: https://arxiv.org/abs/2111.01556 · HTML: https://mlsanity.com/paper/2111.01556/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 19 references that are in the map.
- First posted on arXiv 1 Nov 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [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)
- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [Multiple Instance Learning: A Survey of Problem Characteristics and Applications](https://mlsanity.com/paper/1612.03365/) (2016)
- [Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images](https://mlsanity.com/paper/2001.01599/) (2020)
- [Weakly supervised multiple instance learning histopathological tumor segmentation](https://mlsanity.com/paper/2004.05024/) (2020)
- [SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classification](https://mlsanity.com/paper/2003.05080/) (2020)
- [Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists](https://mlsanity.com/paper/2002.04500/) (2020)
- [AMI-Net+: A Novel Multi-Instance Neural Network for Medical Diagnosis from Incomplete and Imbalanced Data](https://mlsanity.com/paper/1907.01734/) (2019)

## Built on by (most cited first, 30 of 31)

- [Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential](https://mlsanity.com/paper/2206.04425/) (2022)
- [Transcriptomics-guided Slide Representation Learning in Computational Pathology](https://mlsanity.com/paper/2405.11618/) (2024)
- [Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images](https://mlsanity.com/paper/2204.12454/) (2022)
- [Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification](https://mlsanity.com/paper/2210.03664/) (2022)
- [DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2206.08861/) (2022)
- [Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Images](https://mlsanity.com/paper/2202.07001/) (2022)
- [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)
- [Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2301.08125/) (2023)
- [Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Need](https://mlsanity.com/paper/2307.02249/) (2023)
- [Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction](https://mlsanity.com/paper/2205.06672/) (2022)
- [Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer](https://mlsanity.com/paper/2301.02933/) (2023)
- [AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classification](https://mlsanity.com/paper/2406.15303/) (2024)
- [Multiple Instance Learning with Mixed Supervision in Gleason Grading](https://mlsanity.com/paper/2206.12798/) (2022)
- [RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment](https://mlsanity.com/paper/2207.06177/) (2022)
- [Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2408.09476/) (2024)
- [Snuffy: Efficient Whole Slide Image Classifier](https://mlsanity.com/paper/2408.08258/) (2024)
- [Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images](https://mlsanity.com/paper/2301.07700/) (2023)
- [Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images](https://mlsanity.com/paper/2309.09412/) (2023)
- [Towards Hierarchical Regional Transformer-based Multiple Instance Learning](https://mlsanity.com/paper/2308.12634/) (2023)
- [RoFormer for Position Aware Multiple Instance Learning in Whole Slide Image Classification](https://mlsanity.com/paper/2310.01924/) (2023)
- [Semantics-Aware Attention Guidance for Diagnosing Whole Slide Images](https://mlsanity.com/paper/2404.10894/) (2024)
- [ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging](https://mlsanity.com/paper/2306.10535/) (2023)
- [BEL: A Bag Embedding Loss for Transformer enhances Multiple Instance Whole Slide Image Classification](https://mlsanity.com/paper/2303.01377/) (2023)
- [Deep Multiple Instance Learning with Distance-Aware Self-Attention](https://mlsanity.com/paper/2305.10552/) (2023)
- [RACR-MIL: Rank-aware contextual reasoning for weakly supervised grading of squamous cell carcinoma using whole slide images](https://mlsanity.com/paper/2308.15618/) (2023)
- [Enhancing Instance-Level Image Classification with Set-Level Labels](https://mlsanity.com/paper/2311.05659/) (2023)
- [Compact and De-biased Negative Instance Embedding for Multi-Instance Learning on Whole-Slide Image Classification](https://mlsanity.com/paper/2402.10595/) (2024)
- [Exploring learning environments for label-efficient cancer diagnosis](https://mlsanity.com/paper/2408.07988/) (2024)
- [Weakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervision](https://mlsanity.com/paper/2504.12132/) (2025)
- [Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning](https://mlsanity.com/paper/2505.12074/) (2025)

## Abstract (arXiv)

Multiple instance learning (MIL) is a key algorithm for classification of whole slide images (WSI). Histology WSIs can have billions of pixels, which create enormous computational and annotation challenges. Typically, such images are divided into a set of patches (a bag of instances), where only bag-level class labels are provided. Deep learning based MIL methods calculate instance features using convolutional neural network (CNN). Our proposed approach is also deep learning based, with the following two contributions: Firstly, we propose to explicitly account for dependencies between instances during training by embedding self-attention Transformer blocks to capture dependencies between instances. For example, a tumor grade may depend on the presence of several particular patterns at different locations in WSI, which requires to account for dependencies between patches. Secondly, we propose an instance-wise loss function based on instance pseudo-labels. We compare the proposed algorithm to multiple baseline methods, evaluate it on the PANDA challenge dataset, the largest publicly available WSI dataset with over 11K images, and demonstrate state-of-the-art results.