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izvor podataka: crosbi

Transfer Learning Approach for Intraoperative Pixel-based Diagnosis of Colon Cancer Metastasis in a Liver from Hematohylin-Eosin Stained Specimens (CROSBI ID 689014)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Sitnik, Dario ; Kopriva, Ivica ; Aralica, Gorana ; Pačić, Arijana ; Popović Hadžija, Marijana ; Hadžija, Mirko Transfer Learning Approach for Intraoperative Pixel-based Diagnosis of Colon Cancer Metastasis in a Liver from Hematohylin-Eosin Stained Specimens // Proceedings Volume 11320, Medical Imaging 2020: Digital Pathology ; 1132009 (2020) / Tomaszewski, John E. ; Ward, Aaron D. (ur.). Bellingham (WA): SPIE, 2020. doi: 10.1117/12.2538303

Podaci o odgovornosti

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

engleski

Transfer Learning Approach for Intraoperative Pixel-based Diagnosis of Colon Cancer Metastasis in a Liver from Hematohylin-Eosin Stained Specimens

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&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.

UNet ; transfer learning ; segmentation ; intraoperative diagnosis ; hematoxylin-eosin

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Podaci o prilogu

113200A

2020.

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objavljeno

10.1117/12.2538303

Podaci o matičnoj publikaciji

Proceedings Volume 11320, Medical Imaging 2020: Digital Pathology ; 1132009 (2020)

Tomaszewski, John E. ; Ward, Aaron D.

Bellingham (WA): SPIE

Podaci o skupu

SPIE Medical Imaging Symposium 2020

predavanje

15.02.2020-20.02.2020

Houston (TX), Sjedinjene Američke Države

Povezanost rada

Kliničke medicinske znanosti, Računarstvo

Poveznice