# Breast density classification with deep convolutional neural networks

arXiv: https://arxiv.org/abs/1711.03674 · HTML: https://mlsanity.com/paper/1711.03674/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2017.

## Built on by (most cited first, 5 of 5)

- [Three Applications of Conformal Prediction for Rating Breast Density in Mammography](https://mlsanity.com/paper/2206.12008/) (2022)
- [Multi-task fusion for improving mammography screening data classification](https://mlsanity.com/paper/2112.01320/) (2021)
- [Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics](https://mlsanity.com/paper/2405.01822/) (2024)
- [Artificial Intelligence For Breast Cancer Detection: Trends & Directions](https://mlsanity.com/paper/2110.00942/) (2021)
- [A multi-reconstruction study of breast density estimation using Deep Learning](https://mlsanity.com/paper/2202.08238/) (2022)

## Abstract (arXiv)

Breast density classification is an essential part of breast cancer screening. Although a lot of prior work considered this problem as a task for learning algorithms, to our knowledge, all of them used small and not clinically realistic data both for training and evaluation of their models. In this work, we explore the limits of this task with a data set coming from over 200,000 breast cancer screening exams. We use this data to train and evaluate a strong convolutional neural network classifier. In a reader study, we find that our model can perform this task comparably to a human expert.