# A Conformal Prediction Score that is Robust to Label Noise

arXiv: https://arxiv.org/abs/2405.02648 · HTML: https://mlsanity.com/paper/2405.02648/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 4 May 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Classification with Valid and Adaptive Coverage](https://mlsanity.com/paper/2006.02544/) (2020)
- [Uncertainty Sets for Image Classifiers using Conformal Prediction](https://mlsanity.com/paper/2009.14193/) (2020)
- [MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis](https://mlsanity.com/paper/2010.14925/) (2020)
- [Provably End-to-end Label-Noise Learning without Anchor Points](https://mlsanity.com/paper/2102.02400/) (2021)
- [Fair Conformal Predictors for Applications in Medical Imaging](https://mlsanity.com/paper/2109.04392/) (2021)
- [Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets](https://mlsanity.com/paper/2207.02238/) (2022)
- [Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training](https://mlsanity.com/paper/2205.04723/) (2022)

## Built on by (most cited first, 13 of 13)

- [Conformal Prediction: A Data Perspective](https://mlsanity.com/paper/2410.06494/) (2024)
- [Conformal Prediction of Classifiers with Many Classes based on Noisy Labels](https://mlsanity.com/paper/2501.12749/) (2025)
- [Exploring the Noise Robustness of Online Conformal Prediction](https://mlsanity.com/paper/2501.18363/) (2025)
- [Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning](https://mlsanity.com/paper/2410.09878/) (2024)
- [How Many Iterations to Jailbreak? Dynamic Budget Allocation for Multi-Turn LLM Evaluation](https://mlsanity.com/paper/2605.06605/) (2026)
- [Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting](https://mlsanity.com/paper/2505.04733/) (2025)
- [Meta-Analysis with Untrusted Data](https://mlsanity.com/paper/2407.09387/) (2024)
- [Privacy-Preserving Conformal Prediction Under Local Differential Privacy](https://mlsanity.com/paper/2505.15721/) (2025)
- [Efficient Conformal Prediction for Regression Models under Label Noise](https://mlsanity.com/paper/2509.15120/) (2025)
- [When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contamination](https://mlsanity.com/paper/2605.06204/) (2026)
- [Robust Bayes-Assisted Conformal Prediction](https://mlsanity.com/paper/2607.04236/) (2026)
- [Robust Conformalized Selection with Noisy Responses](https://mlsanity.com/paper/2607.22985/) (2026)
- [Dynamic Budget Allocation for LLM Evaluation under Hard Resource Constraints](https://mlsanity.com/paper/2610.07362/) (2026)

## Abstract (arXiv)

Conformal Prediction (CP) quantifies network uncertainty by building a small prediction set with a pre-defined probability that the correct class is within this set. In this study we tackle the problem of CP calibration based on a validation set with noisy labels. We introduce a conformal score that is robust to label noise. The noise-free conformal score is estimated using the noisy labeled data and the noise level. In the test phase the noise-free score is used to form the prediction set. We applied the proposed algorithm to several standard medical imaging classification datasets. We show that our method outperforms current methods by a large margin, in terms of the average size of the prediction set, while maintaining the required coverage.