Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer

By A. Becker, M. Marcon, Soleen Ghafoor, M. Wurnig, T. Frauenfelder, A. Boss. Investigative Radiology, 2017

295 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b757776861a430dafa3d4ad2729f173716c88e23

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In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Danijel Skocaj

Citations in the corpus

Artificial intelligence in retina. (2018, result): “The diagnostic accuracy of a CNN system was shown in 2017 to be comparable to that of experienced radiologists (Becker et al., 2017).”
Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. (2018, methodology): “…were used to train a three layer CNN. 48 Similarly, since then, various studies have investigated different handcrafted features to train CNNs. 4,46,47,61,62 These methods conceptually work in a similar way: combining multiple modules and jointly training all parameters to demonstrate the power of…”
Deep learning with convolutional neural network in radiology (2018, background): “And for some tasks, CNNs achieved the performance almost equal to radiologists [39, 41].”
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology (2018, background): “These statements are best understood from the perspective of advances in a subset of tasks accomplished by radiologists that require specialized intelligence, mainly detection of anomalies [40,53], segmentation [54], and image classification [55].”
Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine (2018, background): “Until now, the clinical application of ML on medical imaging in terms of detection and characterisation have produced results limited to specific tasks, such as differentiation of normal from abnormal chest radiographs [41, 42, 47] or mammograms [48, 49].”
Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening (2019, background): “[35]–[37], some evaluate their models on very small public datasets, InBreast [38] or DDSM [39], which are insufficient for a meaningful evaluation, while others used private datasets with populations of different distributions (on a spectrum between screening population and biopsied subpopulation),”
Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. (2019, result): “In recent years, several deep learning-based algorithms for automated analysis of mammograms have been developed, some of which have already shown very promising results when compared to radiologists, but in very limited and homogeneous scenarios (18,19).”