@inproceedings{9a748c8a13b74554bb21ad8efeaee351,
title = "Deep Features for Tissue-Fold Detection in Histopathology Images",
abstract = "Whole slide imaging (WSI) refers to the digitization of a tissue specimen which enables pathologists to explore high-resolution images on a monitor rather than through a microscope. The formation of tissue folds occur during tissue processing. Their presence may not only cause out-of-focus digitization but can also negatively affect the diagnosis in some cases. In this paper, we have compared five pre-trained convolutional neural networks (CNNs) of different depths as feature extractors to characterize tissue folds. We have also explored common classifiers to discriminate folded tissue against the normal tissue in hematoxylin and eosin (H&E) stained biopsy samples. In our experiments, we manually select the folded area in roughly 2.5 mm 2.5 mm patches at 20x magnification level as the training data. The “DenseNet” with 201 layers alongside an SVM classifier outperformed all other configurations. Based on the leave-one-out validation strategy, we achieved accuracy, whereas with augmentation the accuracy increased to We have tested the generalization of our method with five unseen WSIs from the NIH (National Cancer Institute) dataset. The accuracy for patch-wise detection was One folded patch within an image suffices to flag the entire specimen for visual inspection.",
keywords = "Deep features, Digital pathology, SVM, Tissue folds",
author = "Morteza Babaie and Tizhoosh, {Hamid R.}",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 15th European Congress on Digital Pathology, ECDP 2019 ; Conference date: 10-04-2019 Through 13-04-2019",
year = "2019",
doi = "10.1007/978-3-030-23937-4_15",
language = "English (US)",
isbn = "9783030239367",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "125--132",
editor = "Reyes-Aldasoro, {Constantino Carlos} and Andrew Janowczyk and Mitko Veta and Peter Bankhead and Korsuk Sirinukunwattana",
booktitle = "Digital Pathology - 15th European Congress, ECDP 2019, Proceedings",
}