@inproceedings{dc05304adea14e1c951def3753ca9b6e,
title = "Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learning",
abstract = "Colorectal cancer patients would benefit from a valid, reliable and efficient detection of Tumor Budding (TB), as this is a proven prognostic biomarker. We explored the application of deep learning techniques to detect TB in Hematoxylin and Eosin (H&E) stained slides, and used convolutional neural networks to classify image patches as containing tumor buds, tumor glands and background. As a reference standard for training we stained slides both with H&E and immunohistochemistry (IHC), where one pathologist first annotated buds in IHC and then transferred the obtained annotations to the corresponding H&E image. We show the effectiveness of the proposed three-class approach, which allows to substantially reduce the amount of false positives, especially when combined with a hard-negative mining technique. Finally we report the results of an observer study aimed at investigating the correlation between pathologists at detecting TB in IHC and H&E.",
keywords = "Deep learning, Computational pathology, Colorectal carcinoma, Tumor budding",
author = "J.M. Bokhorst and L. Rijstenberg and D. Goudkade and I. Nagtegaal and {van der Laak}, J. and F. Ciompi",
year = "2018",
doi = "10.1007/978-3-030-00949-6_16",
language = "English",
isbn = "9783030009489",
volume = "11039",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "130--138",
booktitle = "COMPUTATIONAL PATHOLOGY AND OPHTHALMIC MEDICAL IMAGE ANALYSIS",
address = "United States",
note = "1st International Workshop on Computational Pathology (COMPAY) / 5th International Workshop on Ophthalmic Medical Image Analysis (OMIA) ; Conference date: 16-09-2018 Through 20-09-2018",
}