# Independent evaluation of state-of-the-art deep networks for mammography

arXiv: https://arxiv.org/abs/2206.12407 · HTML: https://mlsanity.com/paper/2206.12407/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [Detecting and classifying lesions in mammograms with Deep Learning](https://mlsanity.com/paper/1707.08401/) (2017)
- [An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization](https://mlsanity.com/paper/2002.07613/) (2020)
- [Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach](https://mlsanity.com/paper/1912.11027/) (2019)
- [High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks](https://mlsanity.com/paper/1703.07047/) (2017)
- [A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification](https://mlsanity.com/paper/1707.06978/) (2017)
- [Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis](https://mlsanity.com/paper/2106.07049/) (2021)
- [Meta-repository of screening mammography classifiers](https://mlsanity.com/paper/2108.04800/) (2021)

## Abstract (arXiv)

Deep neural models have shown remarkable performance in image recognition tasks, whenever large datasets of labeled images are available. The largest datasets in radiology are available for screening mammography. Recent reports, including in high impact journals, document performance of deep models at or above that of trained radiologists. What is not yet known is whether performance of these trained models is robust and replicates across datasets. Here we evaluate performance of five published state-of-the-art models on four publicly available mammography datasets. The limited size of public datasets precludes retraining the model and so we are limited to evaluate those models that have been made available with pre-trained parameters. Where test data was available, we replicated published results. However, the trained models performed poorly on out-of-sample data, except when based on all four standard views of a mammographic exam. We conclude that future progress will depend on a concerted effort to make more diverse and larger mammography datasets publicly available. Meanwhile, results that are not accompanied by a release of trained models for independent validation should be judged cautiously.