mlsanityData to 13 Oct 2026

Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions

arXiv 2408.09476 ↗First posted 18 Aug 2024cs.CV cs.LGcited by 7

Not on the map. Why?

Abstract (arXiv)

Whole slide images (WSIs) are gigapixel-scale digital images of H&E-stained tissue samples widely used in pathology. The substantial size and complexity of WSIs pose unique analytical challenges. Multiple Instance Learning (MIL) has emerged as a powerful approach for addressing these challenges, particularly in cancer classification and detection. This survey provides a comprehensive overview of the challenges and methodologies associated with applying MIL to WSI analysis, including attention mechanisms, pseudo-labeling, transformers, pooling functions, and graph neural networks. Additionally, it explores the potential of MIL in discovering cancer cell morphology, constructing interpretable machine learning models, and quantifying cancer grading. By summarizing the current challenges, methodologies, and potential applications of MIL in WSI analysis, this survey aims to inform researchers about the state of the field and inspire future research directions.

Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). Builds on 22 of its 66 references that are in the map. First posted on arXiv 18 Aug 2024.

Builds on

22 of 66 references.

References · 22

  1. 2018Representation Learning with Contrastive Predictive Codingcited by 7,712
  2. 2018Attention-based Deep Multiple Instance Learningcited by 1,083
  3. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 739
  4. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 551
  5. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 474
  6. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 359
  7. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 255
  8. 2020Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networkscited by 178
  9. 2023Artificial Intelligence for Digital and Computational Pathologycited by 107
  10. 2020Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primarycited by 89
  11. 2020Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Imagescited by 81
  12. 2018Neural Image Compression for Gigapixel Histopathology Image Analysiscited by 65
10 more
  1. 2021Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classificationcited by 65
  2. 2020Weakly supervised multiple instance learning histopathological tumor segmentationcited by 46
  3. 2022DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classificationcited by 40
  4. 2019Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Codingcited by 35
  5. 2020A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learningcited by 35
  6. 2021Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imagingcited by 31
  7. 2020RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classificationcited by 28
  8. 2021DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancercited by 27
  9. 2019Deep Instance-Level Hard Negative Mining Model for Histopathology Imagescited by 12
  10. 2022Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance poolingcited by 11

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 6; 2026: 1.

7 papers.

  1. 2025WISE: A Framework for Gigapixel Whole-Slide-Image Lossless Compressioncited by 3
  2. 2025PS3: A Multimodal Transformer Integrating Pathology Reports with Histology Images and Biological Pathways for Cancer Survival Predictioncited by 3
  3. 2025Deformable Attention Graph Representation Learning for Histopathology Whole Slide Image Analysiscited by 2
  4. 2025Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Imagescited by 0
  5. 2025GAS-MIL: Group-Aggregative Selection Multi-Instance Learning for Ensemble of Foundation Models in Digital Pathology Image Analysiscited by 0
  6. 2025CAPRMIL: Context-Aware Patch Representations for Multiple Instance Learningcited by 0
  7. 2026Backdoor Learning in Language Models and Vision-Language Modelscited by 0