Data Fusion of Histological and Immunohistochemical Image Data for Breast Cancer Diagnostics using Transfer Learning

Pranita Pradhan, Pranita Pradhan, Katharina Köhler, Katharina Köhler, Shuxia Guo, Shuxia Guo, Olga Rosin, Olga Rosin, Jürgen Popp, Jürgen Popp, Axel Niendorf, Axel Niendorf, Thomas Bocklitz, Thomas Bocklitz

2021

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.

Paper Citation

in Harvard Style

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 - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 495-506. DOI: 10.5220/0010225504950506

in Bibtex Style

@conference{icpram21,
author={Pranita Pradhan and Katharina Köhler and Shuxia Guo and Olga Rosin and Jürgen Popp and Axel Niendorf and Thomas 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 - Volume 1: ICPRAM,},
year={2021},
pages={495-506},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010225504950506},
isbn={978-989-758-486-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Data Fusion of Histological and Immunohistochemical Image Data for Breast Cancer Diagnostics using Transfer Learning
SN - 978-989-758-486-2
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