# Detecting and classifying lesions in mammograms with Deep Learning

arXiv: https://arxiv.org/abs/1707.08401 · HTML: https://mlsanity.com/paper/1707.08401/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2017.

## Built on by (most cited first, 30 of 47)

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- [Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations](https://mlsanity.com/paper/2101.07945/) (2021)
- [RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement](https://mlsanity.com/paper/2304.00257/) (2023)
- [Meta-repository of screening mammography classifiers](https://mlsanity.com/paper/2108.04800/) (2021)
- [Multi-task fusion for improving mammography screening data classification](https://mlsanity.com/paper/2112.01320/) (2021)
- [Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detection](https://mlsanity.com/paper/2209.05809/) (2022)
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- [TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detection](https://mlsanity.com/paper/2311.05192/) (2023)
- [Breast Mass Detection with Faster R-CNN: On the Feasibility of Learning from Noisy Annotations](https://mlsanity.com/paper/2104.12218/) (2021)
- [Multimodal Breast Lesion Classification Using Cross-Attention Deep Networks](https://mlsanity.com/paper/2108.09591/) (2021)
- [A survey on deep learning approaches for breast cancer diagnosis](https://mlsanity.com/paper/2109.08853/) (2021)
- [DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detection](https://mlsanity.com/paper/2203.06886/) (2022)
- [An Interactive Interpretability System for Breast Cancer Screening with Deep Learning](https://mlsanity.com/paper/2210.08979/) (2022)
- [Improving Mass Detection in Mammography Images: A Study of Weakly Supervised Learning and Class Activation Map Methods](https://mlsanity.com/paper/2308.03486/) (2023)
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## Abstract (arXiv)

In the last two decades Computer Aided Diagnostics (CAD) systems were developed to help radiologists analyze screening mammograms. The benefits of current CAD technologies appear to be contradictory and they should be improved to be ultimately considered useful. Since 2012 deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions. Deep CNN-s have the potential to revolutionize medical image analysis. We propose a CAD system based on one of the most successful object detection frameworks, Faster R-CNN. The system detects and classifies malignant or benign lesions on a mammogram without any human intervention. The proposed method sets the state of the art classification performance on the public INbreast database, AUC = 0.95 . The approach described here has achieved the 2nd place in the Digital Mammography DREAM Challenge with AUC = 0.85 . When used as a detector, the system reaches high sensitivity with very few false positive marks per image on the INbreast dataset. Source code, the trained model and an OsiriX plugin are availaible online at https://github.com/riblidezso/frcnn_cad .