mlsanityData to 7 Oct 2026

Hard Sample Aware Noise Robust Learning for Histopathology Image Classification

arXiv 2112.03694 ↗First posted 5 Dec 2021eess.IV cs.AI cs.CVcited by 48

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Abstract (arXiv)

Deep learning-based histopathology image classification is a key technique to help physicians in improving the accuracy and promptness of cancer diagnosis. However, the noisy labels are often inevitable in the complex manual annotation process, and thus mislead the training of the classification model. In this work, we introduce a novel hard sample aware noise robust learning method for histopathology image classification. To distinguish the informative hard samples from the harmful noisy ones, we build an easy/hard/noisy (EHN) detection model by using the sample training history. Then we integrate the EHN into a self-training architecture to lower the noise rate through gradually label correction. With the obtained almost clean dataset, we further propose a noise suppressing and hard enhancing (NSHE) scheme to train the noise robust model. Compared with the previous works, our method can save more clean samples and can be directly applied to the real-world noisy dataset scenario without using a clean subset. Experimental results demonstrate that the proposed scheme outperforms the current state-of-the-art methods in both the synthetic and real-world noisy datasets. The source code and data are available at https://github.com/bupt-ai-cz/HSA-NRL/.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). Builds on 29 of its 49 references that are in the map. First posted on arXiv 5 Dec 2021.

Builds on

29 of 49 references.

References · 29

  1. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  2. 2016Understanding deep learning requires rethinking generalizationcited by 1,657
  3. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
  4. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
  5. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,053
  6. 2018Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labelscited by 994
  7. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 691
  8. 2018An Empirical Study of Example Forgetting during Deep Neural Network Learningcited by 669
  9. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 663
  10. 2017MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labelscited by 652
  11. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 580
  12. 2016Training Region-based Object Detectors with Online Hard Example Miningcited by 577
17 more
  1. 2019Symmetric Cross Entropy for Robust Learning with Noisy Labelscited by 499
  2. 2017Robust Loss Functions under Label Noise for Deep Neural Networkscited by 441
  3. 2014Training Deep Neural Networks on Noisy Labels with Bootstrappingcited by 322
  4. 2018Joint Optimization Framework for Learning with Noisy Labelscited by 293
  5. 2017WebVision Database: Visual Learning and Understanding from Web Datacited by 262
  6. 2017Decoupling "when to update" from "how to update"cited by 230
  7. 2018Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noisecited by 216
  8. 2019Probabilistic End-to-end Noise Correction for Learning with Noisy Labelscited by 210
  9. 2019Understanding and Utilizing Deep Neural Networks Trained with Noisy Labelscited by 209
  10. 2017CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noisecited by 181
  11. 2019SELF: Learning to Filter Noisy Labels with Self-Ensemblingcited by 164
  12. 2018Learning to Learn from Noisy Labeled Datacited by 160
  13. 2019Deep Self-Learning From Noisy Labelscited by 122
  14. 2017Learning From Noisy Large-Scale Datasets With Minimal Supervisioncited by 115
  15. 2019Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classificationcited by 33
  16. 2019L_DMI: An Information-theoretic Noise-robust Loss Functioncited by 26
  17. 2018Limited Gradient Descent: Learning With Noisy Labelscited by 4

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Citing papers per year: 2022: 5; 2023: 14; 2024: 12; 2025: 12; 2026: 5.

48 papers.

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  1. 2022Locality Guidance for Improving Vision Transformers on Tiny Datasetscited by 33
  2. 2024Knowledge-enhanced Pretraining for Vision-language Pathology Foundation Model on Cancer Diagnosiscited by 21
  3. 2023Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoderscited by 17
  4. 2024Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarioscited by 16
  5. 2023ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysiscited by 15
  6. 2023Fairness-Aware Client Selection for Federated Learningcited by 15
  7. 2023Auto-Prox: Training-Free Vision Transformer Architecture Search via Automatic Proxy Discoverycited by 13
  8. 2025A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Modelcited by 12
  9. 2023Multi-Head Multi-Loss Model Calibrationcited by 8
  10. 2024A Systematic Review of Generalization Research in Medical Image Classificationcited by 8
  11. 2025PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Taskscited by 8
  12. 2024Learning to Complement and to Defer to Multiple Userscited by 7
36 more
  1. 2025How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmarkcited by 7
  2. 2022Learning with Noisy Labels over Imbalanced Subpopulationscited by 5
  3. 2023Learning to Complement with Multiple Humanscited by 5
  4. 2025Medical Knowledge Intervention Prompt Tuning for Medical Image Classificationcited by 5
  5. 2023GSB: Group Superposition Binarization for Vision Transformer with Limited Training Samplescited by 4
  6. 2023Differences Between Hard and Noisy-labeled Samples: An Empirical Studycited by 4
  7. 2023Dynamically Scaled Temperature in Self-Supervised Contrastive Learningcited by 4
  8. 2023Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learningcited by 4
  9. 2024Prompt-Guided Foundation Model Tuning for Pathology Image Classificationcited by 4
  10. 2025Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathologycited by 4
  11. 2022Denoising Multi-Similarity Formulation: A Self-paced Curriculum-Driven Approach for Robust Metric Learningcited by 3
  12. 2023TVT: Training-Free Vision Transformer Search on Tiny Datasetscited by 3
  13. 2024ReViT: Enhancing Vision Transformers Feature Diversity with Attention Residual Connectionscited by 3
  14. 2024Sparsity- and Hybridity-Inspired Visual Parameter-Efficient Fine-Tuning for Medical Diagnosiscited by 3
  15. 2026Fatigue-Aware Learning to Defer via Constrained Optimisationcited by 3
  16. 2023Incorporating Pre-training Data Matters in Unsupervised Domain Adaptationcited by 2
  17. 2025MSCViT: A Small-size ViT architecture with Multi-Scale Self-Attention Mechanism for Tiny Datasetscited by 2
  18. 2026L2CU: Learning to Complement Unseen Userscited by 2
  19. 2026When More Experts Hurt: Underfitting in Multi-Expert Learning to Defercited by 2
  20. 2022Vision Transformer for Contrastive Clusteringcited by 1
  21. 2024Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image Classificationcited by 1
  22. 2024Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classificationcited by 1
  23. 2024Exploring Compositional Generalization of Multimodal LLMs for Medical Imagingcited by 1
  24. 2025Open set label noise learning with robust sample selection and margin-guided modulecited by 1
  25. 2025Unifying Multiple Foundation Models for Advanced Computational Pathologycited by 1
  26. 2025Bayesian Inference for Correlated Human Experts and Classifierscited by 1
  27. 2025Forging a Dynamic Memory: Retrieval-Guided Continual Learning for Generalist Medical Foundation Modelscited by 1
  28. 2026OpenMedQ: Broad Open Pretraining for Medical Vision-Language Modelscited by 1
  29. 2022LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sectionscited by 0
  30. 2023Self-Supervised Versus Supervised Training for Segmentation of Organoid Imagescited by 0
  31. 2023Sample selection with noise rate estimation in noise learning of medical image analysiscited by 0
  32. 2024Learning Representations for Clustering via Partial Information Discrimination and Cross-Level Interactioncited by 0
  33. 2024SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Losscited by 0
  34. 2025Beyond H&E: Unlocking Pathological Insights with Polarization Imagingcited by 0
  35. 2025A Hybrid Framework Bridging CNN and ViT based on Theory of Evidence for Diabetic Retinopathy Gradingcited by 0
  36. 2026Grid-Preserving Knowledge Distillation: Transferring Convolutional Inductive Bias to Vision Transformers under Data Scarcitycited by 0