Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images: Supplementary MaterialHistopathological examination is today's gold standard for cancer diagnosis. However, this task is time consuming and prone to errors as it requires a detailed visual inspection and interpretation of a pathologist. Digital pathology aims at alleviating these problems by providing computerized methods that quantitatively analyze digitized histopathological tissue images. The performance of these methods mainly rely on features that they use, and thus, their success strictly depends on the ability of these features successfully quantifying the histopathology domain. This technical report contains the supplementary material for the new unsupervised feature extractor that we developed for effective representation and classification of histopathological tissue images .
Index Terms: Deep learning, feature learning, histopathological image representation, digital pathology, automated cancer diagnosis, saliency, colon cancer, hematoxylin-eosin staining.