at Google ScholarTitle data
Pradhan, Pranita ; Köhler, Katharina ; Guo, Shuxia ; Rosin, Olga ; Popp, Jürgen ; Niendorf, Axel ; Bocklitz, Thomas:
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. -
Sétubal
: SciTePress
,
2021
. - pp. 495-506
ISBN 978-989-758-486-2
DOI: https://doi.org/10.5220/0010225504950506
Abstract in another language
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 pretrained inception and residual networks with transfer learning strategy 1 achieved the best breast cancer detection.
Further data
| Item Type: | Article in a book |
|---|---|
| Refereed: | Yes |
| Keywords: | Breast Cancer; Transfer Learning; Histology; Immunohistochemistry |
| Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz |
| Result of work at the UBT: | No |
| DDC Subjects: | 000 Computer Science, information, general works > 004 Computer science 500 Science > 530 Physics |
| Date Deposited: | 11 May 2023 12:44 |
| Last Modified: | 11 May 2023 12:44 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76398 |