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.
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