Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks

TL;DR

This paper demonstrates how deep convolutional neural networks, combined with transfer learning and data augmentation, can effectively classify breast masses in mammograms, achieving state-of-the-art accuracy and surpassing human performance.

Contribution

The study introduces a CNN-based approach with transfer learning and data augmentation for breast mass classification, achieving superior results on the DDSM dataset.

Findings

Achieved state-of-the-art accuracy on DDSM dataset

Surpassed human performance in classification tasks

Provided interpretability of the CNN model

Abstract

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.