mlsanityData to 14 Oct 2026

LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation

arXiv 2502.02707 ↗First posted 4 Feb 2025cs.CVcited by 0

On the map: Knowledge distillation → Distillation for medical imaging

Abstract (arXiv)

Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in computational pathology often neglects instance-level learning as supervision is typically provided only at the bag level, hindering the integrated consideration of instance and bag-level information during the analysis. In this work, we present LadderMIL, a framework designed to improve MIL through two perspectives: (1) employing instance-level supervision and (2) learning inter-instance contextual information at bag level. Firstly, we propose a novel Coarse-to-Fine Self-Distillation (CFSD) paradigm that probes and distils a network trained with bag-level information to adaptively obtain instance-level labels which could effectively provide the instance-level supervision for the same network in a self-improving way. Secondly, to capture inter-instance contextual information in WSI, we propose a Contextual Encoding Generator (CEG), which encodes the contextual appearance of instances within a bag. We also theoretically and empirically prove the instance-level learnability of CFSD. Our LadderMIL is evaluated on multiple clinically relevant benchmarking tasks including breast cancer receptor status classification, multi-class subtype classification, tumour classification, and prognosis prediction. Average improvements of 8.1%, 11% and 2.4% in AUC, F1-score, and C-index, respectively, are demonstrated across the five benchmarks, compared to the best baseline.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation. Builds on 29 of its 69 references that are in the map. First posted on arXiv 4 Feb 2025.

Builds on

29 of 69 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification7 Oct
  2. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification22 Mar

Other references · 27

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  5. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  6. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  7. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  8. 2021CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classificationcited by 806
  9. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  10. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  11. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  12. 2021Nystr"omformer: A Nystr"om-Based Algorithm for Approximating Self-Attentioncited by 471
15 more
  1. 2021Conditional Positional Encodings for Vision Transformerscited by 406
  2. 2019Lookahead Optimizer: k steps forward, 1 step backcited by 336
  3. 2019Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosiscited by 165
  4. 2019Deep neural network models for computational histopathology: A surveycited by 165
  5. 2024HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysiscited by 107
  6. 2023Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Imagescited by 91
  7. 2024Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathologycited by 67
  8. 2024A Clinical Benchmark of Public Self-Supervised Pathology Foundation Modelscited by 66
  9. 2023Attention-Challenging Multiple Instance Learning for Whole Slide Image Classificationcited by 61
  10. 2022Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathologycited by 58
  11. 2022Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learningcited by 22
  12. 2023Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teachercited by 9
  13. 2023Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classificationcited by 8
  14. 2024F2FLDM: Latent Diffusion Models with Histopathology Pre-Trained Embeddings for Unpaired Frozen Section to FFPE Translationcited by 6
  15. 2024Hard Negative Sample Mining for Whole Slide Image Classificationcited by 4

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  1. 2025Dual-stream attention-guided learning for weakly supervised whole slide image classification
  2. 2025Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning
  3. 2026SlideMix: Enhancing Whole Slide Image Analysis via Multimodal Shuffling
  4. 2025Prototype-Based Multiple Instance Learning for Gigapixel Whole Slide Image Classification
  5. 2024Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification
  6. 2025SemaMIL: Semantic-Aware Multiple Instance Learning with Retrieval-Guided State Space Modeling for Whole Slide Images
  7. 2026LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification
  8. 2024MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models