Deep features for breast cancer histopathological image classification

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Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network (2018, background): “2% at the patient level and image level respectively [16].”
Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges (2019, methodology): “Meanwhile, other studies (Araujo et al. 2017; Cheng et al. 2016; Duraisamy and Emperumal 2017; Feng et al. 2018; Gandomkar et al. 2018; Kumar et al. 2017a; Spanhol et al. 2016a, 2017; Xu et al. 2016; Zheng et al. 2017) extracted many patches from the original image.”
Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks (2018, methodology): “On the other hand, in [24], Spanhol et al.”
Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module (2019, methodology): “In [14], the author mentions that previously trained CNN reuse is used as a feature vector, and DeCAF features are extracted.”