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Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning

arXiv 2505.12074 ↗First posted 17 May 2025cs.CVcited by 0

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Abstract (arXiv)

Multiple Instance Learning is the predominant method for Whole Slide Image classification in digital pathology, enabling the use of slide-level labels to supervise model training. Although MIL eliminates the tedious fine-grained annotation process for supervised learning, whether it can learn accurate bag- and instance-level classifiers remains a question. To address the issue, instance-level classifiers and instance masks were incorporated to ground the prediction on supporting patches. These methods, while practically improving the performance of MIL methods, may potentially introduce noisy labels. We propose to bridge the gap between commonly used MIL and fully supervised learning by augmenting both the bag- and instance-level learning processes with pseudo-label correction capabilities elicited from weak to strong generalization techniques. The proposed algorithm improves the performance of dual-level MIL algorithms on both bag- and instance-level predictions. Experiments on public pathology datasets showcase the advantage of the proposed methods.

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References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  3. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,311
  4. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  5. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  6. 2016Training Region-based Object Detectors with Online Hard Example Miningcited by 579
  7. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  8. 2019Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learningcited by 510
  9. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  10. 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 467
  11. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 256
  12. 2019Deep neural network models for computational histopathology: A surveycited by 165
19 more
  1. 2021Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networkscited by 124
  2. 2016Revisiting Multiple Instance Neural Networkscited by 119
  3. 2023Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Imagescited by 91
  4. 2022A graph-transformer for whole slide image classificationcited by 84
  5. 2024Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysiscited by 67
  6. 2023Attention-Challenging Multiple Instance Learning for Whole Slide Image Classificationcited by 61
  7. 2023Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classificationcited by 60
  8. 2024Theoretical Analysis of Weak-to-Strong Generalizationcited by 48
  9. 2022Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classificationcited by 43
  10. 2024Quantifying the Gain in Weak-to-Strong Generalizationcited by 41
  11. 2022DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classificationcited by 40
  12. 2021Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imagingcited by 31
  13. 2022Towards 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 Analysiscited by 29
  14. 2024Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Modelscited by 28
  15. 2023Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Needcited by 22
  16. 2022Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalizationcited by 17
  17. 2024A transfer learning framework for weak-to-strong generalizationcited by 14
  18. 2024Bayesian WeakS-to-Strong from Text Classification to Generationcited by 6
  19. 2024Rethinking Multiple Instance Learning: Developing an Instance-Level Classifier via Weakly-Supervised Self-Trainingcited by 1

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  2. 2026LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification
  3. 2026Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning
  4. 2024Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology
  5. 2024Enhancing Weakly-Supervised Histopathology Image Segmentation with Knowledge Distillation on MIL-Based Pseudo-Labels
  6. 2024Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification
  7. 2025Dual-stream attention-guided learning for weakly supervised whole slide image classification
  8. 2025ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification