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

arXiv: https://arxiv.org/abs/2403.13111 · HTML: https://mlsanity.com/paper/2403.13111/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 85 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 22 of 22)

- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Deep learning with noisy labels: exploring techniques and remedies in medical image analysis](https://mlsanity.com/paper/1912.02911/) (2019)
- [Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey](https://mlsanity.com/paper/1912.05170/) (2019)
- [Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods](https://mlsanity.com/paper/2111.02398/) (2021)
- [Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels](https://mlsanity.com/paper/2005.12734/) (2020)
- [Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification](https://mlsanity.com/paper/1901.07759/) (2019)
- [Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment](https://mlsanity.com/paper/1906.07775/) (2019)
- [Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training](https://mlsanity.com/paper/2205.04723/) (2022)
- [Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction](https://mlsanity.com/paper/2109.05159/) (2021)
- [BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification](https://mlsanity.com/paper/2203.01937/) (2022)
- [Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty](https://mlsanity.com/paper/2007.16102/) (2020)
- [Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment](https://mlsanity.com/paper/2104.05261/) (2021)
- [RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging](https://mlsanity.com/paper/2210.08388/) (2022)
- [Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms](https://mlsanity.com/paper/2211.03333/) (2022)
- [Label-noise-tolerant medical image classification via self-attention and self-supervised learning](https://mlsanity.com/paper/2306.09718/) (2023)
- [Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise](https://mlsanity.com/paper/2307.04868/) (2023)
- [Deep Learning from Small Amount of Medical Data with Noisy Labels: A Meta-Learning Approach](https://mlsanity.com/paper/2010.06939/) (2020)
- [Bayesian Statistics Guided Label Refurbishment Mechanism: Mitigating Label Noise in Medical Image Classification](https://mlsanity.com/paper/2106.12284/) (2021)
- [Alternating Loss Correction for Preterm-Birth Prediction from EHR Data with Noisy Labels](https://mlsanity.com/paper/1811.09782/) (2018)
- [Advancing Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI using Noisy Student-based Training](https://mlsanity.com/paper/2111.05959/) (2021)

## Built on by (most cited first, 10 of 10)

- [Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization](https://mlsanity.com/paper/2504.18026/) (2025)
- [Exploring the interplay of label bias with subgroup size and separability: A case study in mammographic density classification](https://mlsanity.com/paper/2507.17996/) (2025)
- [Regretful Decisions under Label Noise](https://mlsanity.com/paper/2504.09330/) (2025)
- [Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation](https://mlsanity.com/paper/2603.26592/) (2026)
- [HyperSORT: Self-Organising Robust Training with hyper-networks](https://mlsanity.com/paper/2506.21430/) (2025)
- [Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation](https://mlsanity.com/paper/2605.10165/) (2026)
- [Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification](https://mlsanity.com/paper/2606.12252/) (2026)
- [Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection](https://mlsanity.com/paper/2606.16868/) (2026)
- [In-Context Learning for Wound Classification with Small Multimodal Language Models](https://mlsanity.com/paper/2607.18819/) (2026)
- [Multimodal Deep Learning for Uncertainty-Aware Radiation Pneumonitis Risk Prediction](https://mlsanity.com/paper/2608.28937/) (2026)

## 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.