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

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

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings Volume 11320, Medical Imaging 2020: Digital Pathology ; 1132009 (2020) / Tomaszewski, John E. ; Ward, Aaron D. - Bellingham (WA) : SPIE, 2020

Skup
SPIE Medical Imaging Symposium 2020

Mjesto i datum
Houston (TX), Sjedinjene Američke Države, 15.02.2020. - 20.02.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

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

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Kliničke medicinske znanosti

POVEZANOST RADA