# Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1612.00542 · HTML: https://mlsanity.com/paper/1612.00542/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2016.

## Built on by (most cited first, 12 of 12)

- [Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback](https://mlsanity.com/paper/2306.12438/) (2023)
- [Improving the repeatability of deep learning models with Monte Carlo dropout](https://mlsanity.com/paper/2202.07562/) (2022)
- [Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations](https://mlsanity.com/paper/2101.07945/) (2021)
- [A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica](https://mlsanity.com/paper/2107.11696/) (2021)
- [Pavementscapes: a large-scale hierarchical image dataset for asphalt pavement damage segmentation](https://mlsanity.com/paper/2208.00775/) (2022)
- [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)
- [Case-level Breast Cancer Prediction for Real Hospital Settings](https://mlsanity.com/paper/2310.12677/) (2023)
- [Use of Transfer Learning and Wavelet Transform for Breast Cancer Detection](https://mlsanity.com/paper/2103.03602/) (2021)
- [A Sneak Attack on Segmentation of Medical Images Using Deep Neural Network Classifiers](https://mlsanity.com/paper/2201.02771/) (2022)
- [Stacking-Enhanced Bagging Ensemble Learning for Breast Cancer Classification with CNN](https://mlsanity.com/paper/2407.10574/) (2024)
- [Computer Aided Detection and Classification of mammograms using Convolutional Neural Network](https://mlsanity.com/paper/2409.16290/) (2024)

## Abstract (arXiv)

Mammography is the most widely used method to screen breast cancer. Because of its mostly manual nature, variability in mass appearance, and low signal-to-noise ratio, a significant number of breast masses are missed or misdiagnosed. In this work, we present how Convolutional Neural Networks can be used to directly classify pre-segmented breast masses in mammograms as benign or malignant, using a combination of transfer learning, careful pre-processing and data augmentation to overcome limited training data. We achieve state-of-the-art results on the DDSM dataset, surpassing human performance, and show interpretability of our model.