Using deep learning to segment breast and fibroglandular tissue in MRI volumes

By M. Dalmis, Geert J. S. Litjens, Katharina Holland, Arnaud Arindra Adiyoso Setio, R. Mann, Nico Karssemeijer, A. Gubern-Mérida. 2017

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Deep Reinforcement Learning for Automated Radiation Adaptation in Lung Cancer (2017, background): “while at the same time providing superior performance [7]-[8].”
A survey on deep learning in medical image analysis (2017, background): “Dalmis et al. (2017) MRI CNN Breast and fibroglandular tissue segmentation”
Deep learning in medical imaging and radiation therapy. (2018, methodology): “Derivatives of U-net have been used for multiple tasks, including segmenting breast and fibroglandular tissue 85 and craniomaxillofacial bony structures.”
Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. (2018, methodology): “62 The U-net is an architecture based on CNN and takes its name because of the “U” shape of the network.”
Deep Learning and Medical Diagnosis: A Review of Literature (2018, unknown): “[52] U-Net MRI Breast and fibroglandular tissue segmentation; average Dice Similarity Coefficients (DSC) were 0.”