Detecting and classifying lesions in mammograms with Deep Learning.
Dezső RibliAnna HorváthZsuzsa UngerPéter PollnerIstvan CsabaiPublished in: Scientific reports (2018)
In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore 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 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 published online at https://github.com/riblidezso/frcnn_cad .
Keyphrases
- convolutional neural network
- deep learning
- coronary artery disease
- artificial intelligence
- endothelial cells
- machine learning
- healthcare
- induced pluripotent stem cells
- randomized controlled trial
- pluripotent stem cells
- loop mediated isothermal amplification
- real time pcr
- magnetic resonance imaging
- mental health
- systematic review
- contrast enhanced
- health information
- computed tomography
- sensitive detection
- adverse drug