# Three Applications of Conformal Prediction for Rating Breast Density in Mammography

arXiv: https://arxiv.org/abs/2206.12008 · HTML: https://mlsanity.com/paper/2206.12008/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 23 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017)
- [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)
- [Least Ambiguous Set-Valued Classifiers with Bounded Error Levels](https://mlsanity.com/paper/1609.00451/) (2016)
- [Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer](https://mlsanity.com/paper/1711.06664/) (2017)
- [Reading Race: AI Recognises Patient's Racial Identity In Medical Images](https://mlsanity.com/paper/2107.10356/) (2021)
- [CheXclusion: Fairness gaps in deep chest X-ray classifiers](https://mlsanity.com/paper/2003.00827/) (2020)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [Fair Conformal Predictors for Applications in Medical Imaging](https://mlsanity.com/paper/2109.04392/) (2021)
- [The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study](https://mlsanity.com/paper/1911.00515/) (2019)
- [Algorithm Fairness in AI for Medicine and Healthcare](https://mlsanity.com/paper/2110.00603/) (2021)
- [CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting](https://mlsanity.com/paper/2002.11379/) (2020)
- [Addressing catastrophic forgetting for medical domain expansion](https://mlsanity.com/paper/2103.13511/) (2021)
- [Breast density classification with deep convolutional neural networks](https://mlsanity.com/paper/1711.03674/) (2017)

## Built on by (most cited first, 6 of 6)

- [Conformal Prediction with Large Language Models for Multi-Choice Question Answering](https://mlsanity.com/paper/2305.18404/) (2023)
- [Federated Conformal Predictors for Distributed Uncertainty Quantification](https://mlsanity.com/paper/2305.17564/) (2023)
- [Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets](https://mlsanity.com/paper/2207.02238/) (2022)
- [Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction](https://mlsanity.com/paper/2302.07675/) (2023)
- [Conformal Off-Policy Evaluation in Markov Decision Processes](https://mlsanity.com/paper/2304.02574/) (2023)
- [Pitfalls of Conformal Predictions for Medical Image Classification](https://mlsanity.com/paper/2506.18162/) (2025)

## Abstract (arXiv)

Breast cancer is the most common cancers and early detection from mammography screening is crucial in improving patient outcomes. Assessing mammographic breast density is clinically important as the denser breasts have higher risk and are more likely to occlude tumors. Manual assessment by experts is both time-consuming and subject to inter-rater variability. As such, there has been increased interest in the development of deep learning methods for mammographic breast density assessment. Despite deep learning having demonstrated impressive performance in several prediction tasks for applications in mammography, clinical deployment of deep learning systems in still relatively rare; historically, mammography Computer-Aided Diagnoses (CAD) have over-promised and failed to deliver. This is in part due to the inability to intuitively quantify uncertainty of the algorithm for the clinician, which would greatly enhance usability. Conformal prediction is well suited to increase reliably and trust in deep learning tools but they lack realistic evaluations on medical datasets. In this paper, we present a detailed analysis of three possible applications of conformal prediction applied to medical imaging tasks: distribution shift characterization, prediction quality improvement, and subgroup fairness analysis. Our results show the potential of distribution-free uncertainty quantification techniques to enhance trust on AI algorithms and expedite their translation to usage.