Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

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Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features (2017, methodology): “The method [57] uses two-stage, coarse-to-fine profiling which significantly reduces computation time, slower than would be desired for any real-time application.”
Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis (2020, background): “…of cancer regions, previous studies applied domain-speciﬁc handcrafted features to depict morphological character, texture, and statistic property of malignant tumor [33], [34], [40] along with the unsupervised clustering methods (e.g., K -means) and feature embedding for classiﬁcation [41].”