mlsanityData to 7 Oct 2026

Deep learning with noisy labels in medical prediction problems: a scoping review

arXiv 2403.13111 ↗First posted 19 Mar 2024cs.LG cs.AIcited by 10

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Abstract (arXiv)

Objectives: Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation. Research involving label uncertainty is also included. Methods: Our scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4 databases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar. Our search terms include "noisy label AND medical / healthcare / clinical", "un-certainty AND medical / healthcare / clinical", and "noise AND medical / healthcare / clinical". Results: A total of 60 papers met inclusion criteria between 2016 and 2023. A series of practical questions in medical research are investigated. These include the sources of label noise, the impact of label noise, the detection of label noise, label noise handling techniques, and their evaluation. Categorization of both label noise detection methods and handling techniques are provided. Discussion: From a methodological perspective, we observe that the medical community has been up to date with the broader deep-learning community, given that most techniques have been evaluated on medical data. We recommend considering label noise as a standard element in medical research, even if it is not dedicated to handling noisy labels. Initial experiments can start with easy-to-implement methods, such as noise-robust loss functions, weighting, and curriculum learning.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 22 of its 85 references that are in the map. First posted on arXiv 19 Mar 2024.

Builds on

22 of 85 references.

References · 22

  1. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  2. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 663
  3. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 580
  4. 2019Deep learning with noisy labels: exploring techniques and remedies in medical image analysiscited by 173
  5. 2019Image Classification with Deep Learning in the Presence of Noisy Labels: A Surveycited by 101
  6. 2021Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methodscited by 83
  7. 2020Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labelscited by 56
  8. 2019Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classificationcited by 33
  9. 2019Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessmentcited by 23
  10. 2022Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-trainingcited by 21
  11. 2021Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correctioncited by 16
  12. 2022BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classificationcited by 9
10 more
  1. 2020Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertaintycited by 8
  2. 2021Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessmentcited by 6
  3. 2022RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imagingcited by 4
  4. 2022Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarmscited by 4
  5. 2023Label-noise-tolerant medical image classification via self-attention and self-supervised learningcited by 4
  6. 2023Leveraging an Alignment Set in Tackling Instance-Dependent Label Noisecited by 4
  7. 2020Deep Learning from Small Amount of Medical Data with Noisy Labels: A Meta-Learning Approachcited by 3
  8. 2021Bayesian Statistics Guided Label Refurbishment Mechanism: Mitigating Label Noise in Medical Image Classificationcited by 2
  9. 2018Alternating Loss Correction for Preterm-Birth Prediction from EHR Data with Noisy Labelscited by 1
  10. 2021Advancing Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI using Noisy Student-based Trainingcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 6.

10 papers.

  1. 2025Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimizationcited by 7
  2. 2025Exploring the interplay of label bias with subgroup size and separability: A case study in mammographic density classificationcited by 2
  3. 2025Regretful Decisions under Label Noisecited by 1
  4. 2026Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotationcited by 1
  5. 2025HyperSORT: Self-Organising Robust Training with hyper-networkscited by 0
  6. 2026Task-Agnostic Noisy Label Detection via Standardized Loss Aggregationcited by 0
  7. 2026Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classificationcited by 0
  8. 2026Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selectioncited by 0
  9. 2026In-Context Learning for Wound Classification with Small Multimodal Language Modelscited by 0
  10. 2026Multimodal Deep Learning for Uncertainty-Aware Radiation Pneumonitis Risk Predictioncited by 0