The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Noise Robust Learning with Hard Example Aware for Pathological Image classification
Abstract
Label noise among the datasets can be memorized by deep neural networks, which degenerates the robustness of the learned model. However, recent studies proposed to deal with label noise are difficult in distinguishing informative hard examples from harmful noise ones, especially for medical image analysis. In this paper, we propose a noise robust learning algorithm based on hard example aware and Co-weighting to circumvent label noise. Firstly, we identify and exclude noisy samples according to the statistical features of predictive history, which allows retaining informative hard examples. Secondly, we adopt Co-weighting strategy for robust training, which improves Co-teaching algorithm through weighting samples between peer networks. Compared with extensive state-of-the-art methods, our strategy achieves the best performance on two colon tumor histology image datasets under different ratios of noise.
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“Label noise can also derive from human uncertainty when using the consensus of multiple annotators, often needed due to the absence of a pathologically confirmed diagnosis label, thereby introducing inherent uncertainty35–37.”
From A scoping review of robustness concepts for machine learning in healthcare (2025) · cites this paper as [36]
Citations
Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Research works printed but not linked: 17
- 10.1109/CVPR.2018.00582 · DOI
- ma, 2018, Dimensionality-driven learning with noisy labels
- chen, 2019, Understanding and utilizing deep neural networks trained with noisy labels
- han, 0, Co-teaching: Robust training of deep neural networks with extremely noisy labels, Advances in neural information processing systems, 8527
- chang, 0, Active bias: Training more accurate neural networks by emphasizing high variance samples, Advances in neural information processing systems, 1002
- li, 0, Signet ring cell detection with a semisupervised learning framework, International Conference on Information Processing in Medical Imaging Springer, 842
- szegedy, 0, Inception-v4, inceptionresnet and the impact of residual connections on learning, Thirty-First AAAI Conference on Artificial Intelligence
- paszke, 2017, Automatic differentiation in pytorch
- tam nguyen, 2019, Robust learning under label noise with iterative noisefiltering
- hendrycks, 0, Using trusted data to train deep networks on labels corrupted by severe noise, Advances in neural information processing systems, 10456
- kumar, 0, Self-paced learning for latent variable models, Advances in neural information processing systems, 1189
- 10.1109/CVPR.2016.89 · DOI · Paper page
- jiang, 2017, Mentornet Learning data-driven curriculum for very deep neural networks on corrupted labels
- ren, 2018, Learning to reweight examples for robust deep learning
- arpit, 0, A closer look at memorization in deep networks, Proceedings of the 34th International Conference on Machine Learning, 70, 233
- zhang, 2016, Understanding deep learning requires rethinking generalization
- toneva, 2018, An empirical study of example forgetting during deep neural network learning
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A scoping review of robustness concepts for machine learning in healthcare
Balendran, A., Beji, C., Bouvier, F., Khalifa, O., Evgeniou, T., Ravaud, P., & Porcher, R. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01420-1 · Cited by 43
Metadata
- Authors
- Peng, T., Zhu, C., Luo, Y., Liu, J., Wang, Y., & Jin, M.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- Beijing Chao-Yang Hospital, Capital Medical University (CN)
- Venue
- 2020 IEEE 6th International Conference on Computer and Communications (ICCC)
- Publisher
- IEEE
- Published
- 2020-12-11
- Volume
- 70
- Pages
- 1903-1907
- OpenAlex
- W3129573010
- Cited by
- 3
- References
- 1
- Citations by year
- 2025: 1 · 2024: 2
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Computer and Communications (ICCC) (publishedVersion)
- Funders
- National Key R&D Program of China (2019YFF0303300,2019YFF0303302)
- Beijing Natural Science Foundation (4182044,7202056)
- National Natural Science Foundation of China (81972248)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- label noise, noise-robust learning, co-teaching, pathological image classification, medical image analysis, peer networks
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- The paper
- Peng, T., Zhu, C., Luo, Y., Liu, J., Wang, Y., & Jin, M. (2020). Noise Robust Learning with Hard Example Aware for Pathological Image classification. 2020 IEEE 6th International Conference on Computer and Communications (ICCC). https://doi.org/10.1109/iccc51575.2020.9344937
