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Published in:
2018 | OriginalPaper | Chapter
Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content
Authors : Sebastian Otálora, Manfredo Atzori, Vincent Andrearczyk, Henning Müller
Published in: Computational Pathology and Ophthalmic Medical Image Analysis
Publisher: Springer International Publishing
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Abstract
Open access medical content databases such as PubMed Central and TCGA offer possibilities to obtain large amounts of images for training deep learning models. Nevertheless, accurate labeling of large-scale medical datasets is not available and poses challenging tasks for using such datasets. Predicting unknown magnification levels and standardize staining procedures is a necessary preprocessing step for using this data in retrieval and classification tasks. In this paper, a CNN-based regression approach to learn the magnification of histopathology images is presented, comparing two deep learning architectures tailored to regress the magnification. A comparison of the performance of the models is done in a dataset of 34,441 breast cancer patches with several magnifications. The best model, a fusion of DenseNet-based CNNs, obtained a kappa score of 0.888. The methods are also evaluated qualitatively on a set of images from biomedical journals and TCGA prostate patches.