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Authors: Pranita Pradhan 1 ; 2 ; Katharina Köhler 3 ; 4 ; Shuxia Guo 1 ; 2 ; Olga Rosin 3 ; 4 ; Jürgen Popp 1 ; 5 ; Axel Niendorf 3 ; 4 and Thomas Wilhelm Bocklitz 1 ; 5

Affiliations: 1 Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, Jena, 07743, Thuringen, Germany ; 2 Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, Jena, 07745, Thüringen, Germany ; 3 Institute for Histology, Cytology and Molecular Diagnostics,Lornsenstraße 4, Hamburg, 22767, Hamburg, Germany ; 4 MVZ Prof. Dr. med. A. Niendorf Pathologie Hamburg-West GmbH, Lornsenstraße 4-6, Hamburg, 22767, Hamburg, Germany ; 5 Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, Jena, 07745, Thuringen, Germany

Keyword(s): Breast Cancer, Transfer Learning, Histology, Immunohistochemistry.

Abstract: A combination of histological and immunohistochemical tissue features can offer better breast cancer diagnosis as compared to histological tissue features alone. However, manual identification of histological and immunohistochemical tissue features for cancerous and healthy tissue requires an enormous human effort which delays the breast cancer diagnosis. In this paper, breast cancer detection using the fusion of histological (H&E) and immunohistochemical (PR, ER, Her2 and Ki-67) imaging data based on deep convolutional neural networks (DCNN) was performed. DCNNs, including the VGG network, the residual network and the inception network were comparatively studied. The three DCNNs were trained using two transfer learning strategies. In transfer learning strategy 1, a pre-trained DCNN was used to extract features from the images of five stain types. In transfer learning strategy 2, the images of the five stain types were used as inputs to a pre-trained multi-input DCNN, and the last layer of the multi-input DCNN was optimized. The results showed that data fusion of H&E and IHC imaging data could increase the mean sensitivity at least by 2% depending on the DCNN model and the transfer learning strategy. Specifically, the pre-trained inception and residual networks with transfer learning strategy 1 achieved the best breast cancer detection. (More)

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Paper citation in several formats:
Pradhan, P. ; Köhler, K. ; Guo, S. ; Rosin, O. ; Popp, J. ; Niendorf, A. and Bocklitz, T. (2021). Data Fusion of Histological and Immunohistochemical Image Data for Breast Cancer Diagnostics using Transfer Learning. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-486-2; ISSN 2184-4313, SciTePress, pages 495-506. DOI: 10.5220/0010225504950506

@conference{icpram21,
author={Pranita Pradhan and Katharina Köhler and Shuxia Guo and Olga Rosin and Jürgen Popp and Axel Niendorf and Thomas Wilhelm Bocklitz},
title={Data Fusion of Histological and Immunohistochemical Image Data for Breast Cancer Diagnostics using Transfer Learning},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2021},
pages={495-506},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010225504950506},
isbn={978-989-758-486-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Data Fusion of Histological and Immunohistochemical Image Data for Breast Cancer Diagnostics using Transfer Learning
SN - 978-989-758-486-2
IS - 2184-4313
AU - Pradhan, P.
AU - Köhler, K.
AU - Guo, S.
AU - Rosin, O.
AU - Popp, J.
AU - Niendorf, A.
AU - Bocklitz, T.
PY - 2021
SP - 495
EP - 506
DO - 10.5220/0010225504950506
PB - SciTePress

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