Detecting and classifying lesions in mammograms with Deep Learning

TL;DR

This paper introduces a deep learning-based CAD system using Faster R-CNN for mammogram lesion detection and classification, achieving state-of-the-art performance and high sensitivity with few false positives.

Contribution

It presents a novel application of Faster R-CNN to mammogram analysis, surpassing traditional CAD methods and setting new benchmarks in accuracy.

Findings

Achieved AUC = 0.95 on INbreast dataset

Secured 2nd place in DREAM Challenge with AUC = 0.85

High sensitivity with few false positives

Abstract

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 .