mlsanityData to 7 Oct 2026

Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification

arXiv 1901.07759 ↗First posted 23 Jan 2019cs.CVcited by 33

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Abstract (arXiv)

Deep neural networks (DNNs) have achieved great success in a wide variety of medical image analysis tasks. However, these achievements indispensably rely on the accurately-annotated datasets. If with the noisy-labeled images, the training procedure will immediately encounter difficulties, leading to a suboptimal classifier. This problem is even more crucial in the medical field, given that the annotation quality requires great expertise. In this paper, we propose an effective iterative learning framework for noisy-labeled medical image classification, to combat the lacking of high quality annotated medical data. Specifically, an online uncertainty sample mining method is proposed to eliminate the disturbance from noisy-labeled images. Next, we design a sample re-weighting strategy to preserve the usefulness of correctly-labeled hard samples. Our proposed method is validated on skin lesion classification task, and achieved very promising results.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2019.

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Citing papers per year: 2021: 10; 2022: 8; 2023: 5; 2024: 6; 2025: 2; 2026: 2.

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  1. 2022UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learningcited by 77
  2. 2021Hard Sample Aware Noise Robust Learning for Histopathology Image Classificationcited by 48
  3. 2021LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environmentcited by 40
  4. 2021Medical Image Segmentation with Limited Supervision: A Review of Deep Network Modelscited by 21
  5. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  6. 2022Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-trainingcited by 21
  7. 2022A survey, review, and future trends of skin lesion segmentation and classificationcited by 20
  8. 2021Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correctioncited by 16
  9. 2021Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimationcited by 13
  10. 2021Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentationcited by 11
  11. 2022L2B: Learning to Bootstrap Robust Models for Combating Label Noisecited by 10
  12. 2024Deep learning with noisy labels in medical prediction problems: a scoping reviewcited by 10
21 more
  1. 2023Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretrainingcited by 8
  2. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  3. 2022Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regimecited by 5
  4. 2022Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challengecited by 5
  5. 2024QMix: Quality-aware Learning with Mixed Noise for Robust Retinal Disease Diagnosiscited by 5
  6. 2024Towards Few-Shot Learning in the Open World: A Review and Beyondcited by 5
  7. 2022Breast Cancer Molecular Subtypes Prediction on Pathological Images with Discriminative Patch Selecting and Multi-Instance Learningcited by 4
  8. 2023Rapid Image Labeling via Neuro-Symbolic Learningcited by 4
  9. 2025Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlookcited by 3
  10. 2021Breast Mass Detection with Faster R-CNN: On the Feasibility of Learning from Noisy Annotationscited by 1
  11. 2021Data Shapley Value for Handling Noisy Labels: An application in Screening COVID-19 Pneumonia from Chest CT Scanscited by 1
  12. 2024How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?cited by 1
  13. 2024Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classificationcited by 1
  14. 2021Wildfire smoke plume segmentation using geostationary satellite imagerycited by 0
  15. 2022Fair Machine Learning in Healthcare: A Reviewcited by 0
  16. 2023Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-aware Learningcited by 0
  17. 2023Sample selection with noise rate estimation in noise learning of medical image analysiscited by 0
  18. 2024Bayesian uncertainty-aware deep learning with noisy labels: Tackling annotation ambiguity in EEG seizure detectioncited by 0
  19. 2025Beyond H&E: Unlocking Pathological Insights with Polarization Imagingcited by 0
  20. 2026Effectiveness of Automatically Curated Dataset in Thyroid Nodules Classification Algorithms Using Deep Learningcited by 0
  21. 2026Noise-aware few-shot learning through bi-directional multi-view prompt alignmentcited by 0