Detecting and classifying lesions in mammograms with Deep Learning

By Dezso Ribli, Anna Horváth, Z. Unger, Péter Pollner, István Csabai. 2017

653 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, methodology): “The CAD system, proposed in [104], outputs an image that is easy to understand by an expert (in this case an oncologist) because this system outputs red bounding-boxes for delimiting those zone of possible lesions, which facilitate”
Bayesian Nonparametric Weight Factorization for Continual Learning (2020, background): “Regardless of how stored samples are used, however, in certain settings, data is private [36] or classiﬁed [26], and keeping data may be considered as violating continual learning criteria.”
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, background): “As a result of these analyzes, detection and diagnosis of diseases such as diabetes mellitus, brain tumor, skin cancer and breast cancer are provided with convenience [36-41].”
Recent advances and clinical applications of deep learning in medical image analysis (2021, background): “Common computer-aided detection (CADe) tasks include detecting lung nodules (Gu et al., 2018; Xie et al., 2019b), breast masses (Akselrod-Ballin et al., 2017; Ribli et al., 2018), lymph nodes (Lin et al., 2019; Zhu et al., 2020b), sclerosis lesions (Nair et al., 2020), etc.”
Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening (2019, neutral): “, 2017) or models based on the variations of RCNN (Ribli et al., 2018).”
What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation (2020, neutral): “, medical lesions segmentation [3, 7, 16,31,42].”