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Efficient pan-cancer whole-slide image classification and outlier detection using convolutional neural networks

Seda Bilaloglu, Joyce Wu, Eduardo Fierro, Raul Delgado Sanchez, Paolo Santiago Ocampo, Narges Razavian, Nicolas Coudray, Aristotelis Tsirigos
doi: https://doi.org/10.1101/633123
Seda Bilaloglu
1Center for Data Science, New York University, New York, NY, USA
2Department of Population Health, New York University School of Medicine, New York, NY, USA
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Joyce Wu
1Center for Data Science, New York University, New York, NY, USA
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Eduardo Fierro
1Center for Data Science, New York University, New York, NY, USA
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Raul Delgado Sanchez
1Center for Data Science, New York University, New York, NY, USA
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Paolo Santiago Ocampo
5Department of Pathology, New York University School of Medicine, New York, NY, USA
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Narges Razavian
2Department of Population Health, New York University School of Medicine, New York, NY, USA
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Nicolas Coudray
3Applied Bioinformatics Laboratories, New York University School of Medicine, New York, NY, USA
4Skirball Institute, Department of Cell Biology, New York University School of Medicine, New York, NY, USA
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  • For correspondence: Aristotelis.Tsirigos@nyulangone.org Nicolas.Coudray@nyulangone.org
Aristotelis Tsirigos
3Applied Bioinformatics Laboratories, New York University School of Medicine, New York, NY, USA
5Department of Pathology, New York University School of Medicine, New York, NY, USA
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  • For correspondence: Aristotelis.Tsirigos@nyulangone.org Nicolas.Coudray@nyulangone.org
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Abstract

Visual analysis of solid tissue mounted on glass slides is currently the primary method used by pathologists for determining the stage, type and subtypes of cancer. Although whole slide images are usually large (10s to 100s thousands pixels wide), an exhaustive though time-consuming assessment is necessary to reduce the risk of misdiagnosis. In an effort to address the many diagnostic challenges faced by trained experts, recent research has been focused on developing automatic prediction systems for this multi-class classification problem. Typically, complex convolutional neural network (CNN) architectures, such as Google’s Inception, are used to tackle this problem. Here, we introduce a greatly simplified CNN architecture, PathCNN, which allows for more efficient use of computational resources and better classification performance. Using this improved architecture, we trained simultaneously on whole-slide images from multiple tumor sites and corresponding non-neoplastic tissue. Dimensionality reduction analysis of the weights of the last layer of the network capture groups of images that faithfully represent the different types of cancer, highlighting at the same time differences in staining and capturing outliers, artifacts and misclassification errors. Our code is available online at: https://github.com/sedab/PathCNN.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted May 14, 2019.
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Efficient pan-cancer whole-slide image classification and outlier detection using convolutional neural networks
Seda Bilaloglu, Joyce Wu, Eduardo Fierro, Raul Delgado Sanchez, Paolo Santiago Ocampo, Narges Razavian, Nicolas Coudray, Aristotelis Tsirigos
bioRxiv 633123; doi: https://doi.org/10.1101/633123
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Efficient pan-cancer whole-slide image classification and outlier detection using convolutional neural networks
Seda Bilaloglu, Joyce Wu, Eduardo Fierro, Raul Delgado Sanchez, Paolo Santiago Ocampo, Narges Razavian, Nicolas Coudray, Aristotelis Tsirigos
bioRxiv 633123; doi: https://doi.org/10.1101/633123

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