# Hard Sample Aware Noise Robust Learning for Histopathology Image Classification

arXiv: https://arxiv.org/abs/2112.03694 · HTML: https://mlsanity.com/paper/2112.03694/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 49 references that are in the map.
- First posted on arXiv 5 Dec 2021.

## Builds on (most cited first, 29 of 29)

- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [DivideMix: Learning with Noisy Labels as Semi-supervised Learning](https://mlsanity.com/paper/2002.07394/) (2020)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [Training Deep Neural Networks on Noisy Labels with Bootstrapping](https://mlsanity.com/paper/1412.6596/) (2014)
- [Joint Optimization Framework for Learning with Noisy Labels](https://mlsanity.com/paper/1803.11364/) (2018)
- [WebVision Database: Visual Learning and Understanding from Web Data](https://mlsanity.com/paper/1708.02862/) (2017)
- [Decoupling "when to update" from "how to update"](https://mlsanity.com/paper/1706.02613/) (2017)
- [Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise](https://mlsanity.com/paper/1802.05300/) (2018)
- [Probabilistic End-to-end Noise Correction for Learning with Noisy Labels](https://mlsanity.com/paper/1903.07788/) (2019)
- [Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels](https://mlsanity.com/paper/1905.05040/) (2019)
- [CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise](https://mlsanity.com/paper/1711.07131/) (2017)
- [SELF: Learning to Filter Noisy Labels with Self-Ensembling](https://mlsanity.com/paper/1910.01842/) (2019)
- [Learning to Learn from Noisy Labeled Data](https://mlsanity.com/paper/1812.05214/) (2018)
- [Deep Self-Learning From Noisy Labels](https://mlsanity.com/paper/1908.02160/) (2019)
- [Learning From Noisy Large-Scale Datasets With Minimal Supervision](https://mlsanity.com/paper/1701.01619/) (2017)
- [Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification](https://mlsanity.com/paper/1901.07759/) (2019)
- [L_DMI: An Information-theoretic Noise-robust Loss Function](https://mlsanity.com/paper/1909.03388/) (2019)
- [Limited Gradient Descent: Learning With Noisy Labels](https://mlsanity.com/paper/1811.08117/) (2018)

## Built on by (most cited first, 30 of 48)

- [Locality Guidance for Improving Vision Transformers on Tiny Datasets](https://mlsanity.com/paper/2207.10026/) (2022)
- [Knowledge-enhanced Pretraining for Vision-language Pathology Foundation Model on Cancer Diagnosis](https://mlsanity.com/paper/2412.13126/) (2024)
- [Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders](https://mlsanity.com/paper/2310.20704/) (2023)
- [Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarios](https://mlsanity.com/paper/2410.16038/) (2024)
- [ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis](https://mlsanity.com/paper/2304.01053/) (2023)
- [Fairness-Aware Client Selection for Federated Learning](https://mlsanity.com/paper/2307.10738/) (2023)
- [Auto-Prox: Training-Free Vision Transformer Architecture Search via Automatic Proxy Discovery](https://mlsanity.com/paper/2312.09059/) (2023)
- [A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model](https://mlsanity.com/paper/2507.17303/) (2025)
- [PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Tasks](https://mlsanity.com/paper/2503.24345/) (2025)
- [Multi-Head Multi-Loss Model Calibration](https://mlsanity.com/paper/2303.01099/) (2023)
- [A Systematic Review of Generalization Research in Medical Image Classification](https://mlsanity.com/paper/2403.12167/) (2024)
- [How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmark](https://mlsanity.com/paper/2503.12990/) (2025)
- [Learning to Complement and to Defer to Multiple Users](https://mlsanity.com/paper/2407.07003/) (2024)
- [Learning with Noisy Labels over Imbalanced Subpopulations](https://mlsanity.com/paper/2211.08722/) (2022)
- [Learning to Complement with Multiple Humans](https://mlsanity.com/paper/2311.13172/) (2023)
- [Medical Knowledge Intervention Prompt Tuning for Medical Image Classification](https://mlsanity.com/paper/2511.12639/) (2025)
- [GSB: Group Superposition Binarization for Vision Transformer with Limited Training Samples](https://mlsanity.com/paper/2305.07931/) (2023)
- [Differences Between Hard and Noisy-labeled Samples: An Empirical Study](https://mlsanity.com/paper/2307.10718/) (2023)
- [Dynamically Scaled Temperature in Self-Supervised Contrastive Learning](https://mlsanity.com/paper/2308.01140/) (2023)
- [Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning](https://mlsanity.com/paper/2309.06439/) (2023)
- [Prompt-Guided Foundation Model Tuning for Pathology Image Classification](https://mlsanity.com/paper/2403.12537/) (2024)
- [Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology](https://mlsanity.com/paper/2503.18709/) (2025)
- [Denoising Multi-Similarity Formulation: A Self-paced Curriculum-Driven Approach for Robust Metric Learning](https://mlsanity.com/paper/2211.11751/) (2022)
- [TVT: Training-Free Vision Transformer Search on Tiny Datasets](https://mlsanity.com/paper/2311.14337/) (2023)
- [ReViT: Enhancing Vision Transformers Feature Diversity with Attention Residual Connections](https://mlsanity.com/paper/2402.11301/) (2024)
- [Sparsity- and Hybridity-Inspired Visual Parameter-Efficient Fine-Tuning for Medical Diagnosis](https://mlsanity.com/paper/2405.17877/) (2024)
- [When More Experts Hurt: Underfitting in Multi-Expert Learning to Defer](https://mlsanity.com/paper/2602.17144/) (2026)
- [Fatigue-Aware Learning to Defer via Constrained Optimisation](https://mlsanity.com/paper/2604.00904/) (2026)
- [Incorporating Pre-training Data Matters in Unsupervised Domain Adaptation](https://mlsanity.com/paper/2308.03097/) (2023)
- [MSCViT: A Small-size ViT architecture with Multi-Scale Self-Attention Mechanism for Tiny Datasets](https://mlsanity.com/paper/2501.06040/) (2025)

## 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/.