Transfer learning approach for intraoperative pixel-based diagnosis of colon cancer metastasis in a liver from hematoxylin-eosin stained specimens

Dario Sitnik; Ivica Kopriva; Gorana Aralica; Arijana Pačić; Marijana Popović Hadžija; Mirko Hadžija

doi:10.1117/12.2538303

16 March 2020 Transfer learning approach for intraoperative pixel-based diagnosis of colon cancer metastasis in a liver from hematoxylin-eosin stained specimens

Dario Sitnik, Ivica Kopriva, Gorana Aralica, Arijana Pačić, Marijana Popović Hadžija, Mirko Hadžija

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Proceedings Volume 11320, Medical Imaging 2020: Digital Pathology; 113200A (2020) https://doi.org/10.1117/12.2538303
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

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Dario Sitnik, Ivica Kopriva, Gorana Aralica, Arijana Pačić, Marijana Popović Hadžija, and Mirko Hadžija "Transfer learning approach for intraoperative pixel-based diagnosis of colon cancer metastasis in a liver from hematoxylin-eosin stained specimens", Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200A (16 March 2020); https://doi.org/10.1117/12.2538303

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