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Proceedings Paper

Transfer learning approach for intraoperative pixel-based diagnosis of colon cancer metastasis in a liver from hematoxylin-eosin stained specimens
Author(s): Dario Sitnik; Ivica Kopriva; Gorana Aralica; Arijana Pačić; Marijana Popović Hadžija; Mirko Hadžija
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Paper Abstract

Development of computer-aided diagnosis (CAD) systems is motivated by reduction of the workload on the pathologist that is increasing steadily. Among approaches upon which CAD-based systems are built, deep learning (DL) methods seem to be well suited for image analysis in digital pathology. However, DL networks include a large number of parameters and that requires a large annotated training dataset. Unfortunately, probably the biggest problem in digital pathology using machine learning methods is a small number of annotated images. That is especially true in intraoperative tissue analysis which coincides with the topic of the present paper: intraoperative CAD-based diagnosis of metastasis of colon cancer in a liver from hematoxylin-eosin (H and E) stained frozen section. To cope with the insufficiency of training images we adopt a transfer learning approach using the Nested UNet architecture. For better diagnostic performance, the trained model predicted pixels multiple times for different striding levels using the sliding window strategy. Threshold optimization using balanced accuracy score showed the validity of such an approach as balanced accuracy has increased significantly. When compared to often used UNet with VGG16 backbone, Nested UNet model with DenseNet201 backbone performs better on our dataset for both balanced accuracy metric and F1 score.

Paper Details

Date Published: 16 March 2020
PDF: 11 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200A (16 March 2020); doi: 10.1117/12.2538303
Show Author Affiliations
Dario Sitnik, Ruđer Bošković Institute (Croatia)
Ivica Kopriva, Ruđer Bošković Institute (Croatia)
Gorana Aralica, Clinical Hospital Dubrava (Croatia)
Arijana Pačić, Clinical Hospital Dubrava (Croatia)
Marijana Popović Hadžija, Ruđer Bošković Institute (Croatia)
Mirko Hadžija, Ruđer Bošković Institute (Croatia)

Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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