The Automated Learning of Deep Features for Breast Mass Classification from Mammograms

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Opportunities and obstacles for deep learning in biology and medicine (2017, background): “An alternative in the domain is to train towards human-created features before subsequent fine tuning [58], which can help to sidestep this challenge though it does give up deep learning techniques' strength as feature constructors.”
A survey on deep learning in medical image analysis (2017, methodology): “Zhou et al. (2016) Thorax-abdomen segmentation CT CNN 21 structures are segmented with 3 orthogonal 2D fCNNs and majority voting”
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification (2016, methodology): “trained a detector and segmentation network on the training set first, and then used CNN to perform mass classification [7].”
A deep learning approach for the analysis of masses in mammograms with minimal user intervention (2017, methodology): “This paper is an extension of our previous works on mass etection ( Dhungel et al., 2015a ), segmentation ( Dhungel et al., 015b ), and classification ( Dhungel et al., 2016 ) (see Fig.”
Breast cancer detection using deep convolutional neural networks and support vector machines (2019, methodology): “Moreover, the deep learning methods were mentioned in some papers for breast cancer classification as in Dhungel, Carneiro & Bradley (2017a) , Dhungel, Carneiro & Bradley (2017b) , Dhungel, Carneiro & Bradley (2016) , and Ching et al. (2017) .”