Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring

By Michiel Kallenberg, Kersten Petersen, Mads Nielsen, Andrew Y. Ng, Pengfei Diao, Christian Igel, Celine M. Vachon, Katharina Holland, R. Winkel, Nico Karssemeijer and 1 more. IEEE Transactions on Medical Imaging, 2016

390 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/47c18899d0d264ff72ca2376ed8bb2d821f13a98

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In Artificial Intelligence

In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique (2016, neutral): “[32].”
Deep Learning and Its Applications in Biomedicine (2018, background): “Other applications include that of Li et al. [134] and Liu et al. [151,152], who proposed deep learning approaches for the identiﬁcation of cis -regulatory regions and replication timing domains, respectively.”
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017, methodology): “The convolution operation in CNN is a subset of deep learning which has attracted a lot of attention in recent year and used in image recognition such as analysis of x-ray medical images [22], magnetic resonance images [23], histopathological images [24], fundus images [25], and computed tomography ”
Deep Learning Applications in Medical Image Analysis (2018, background): “[35] combined unsupervised convolution layers trained as autoencoders, and supervised layers to classify mammograms into different densities and textures.”
Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer (2017, methodology): “As summarized in Table 2, the diagnostic accuracy achieved by our dANN is comparable to other recently published deep learning models.(9,24,25) In contrast to the present article, these studies make use of specialized dANNs for breast cancer detection.”
Adversarial Deep Structural Networks for Mammographic Mass Segmentation (2016, background): “on rely heavily on elaborate human designed features. Model-based methods build classiﬁers and learn the features from the mass [3,4]. There are few works using deep networks to process the mammogram [12,9,23]. Dhungel et al. employed multiple deep belief networks (DBNs), GMM classiﬁer and a priori ”
Deep Learning in Medical Imaging: General Overview (2017, methodology): “Deep learning has been applied for the identification, detection, and diagnosis and risk analysis of breast cancer (86, 92, 93).”
Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology (2017, background): “Similar concepts have been applied to mammograms for breast cancer screening [53, 54].”
Deep Feature Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network (2017, neutral): “propose a convolutional autoencoder approach to analyze breast images [31], and Li et al.”
Artificial intelligence in radiology (2018, neutral): “Others employ sparse autoencoders to segment breast density and score mammographic texture in an unsupervised manner 98 .”