Liu, J., Xu, B., Zheng, C., Gong, Y., Garibaldi, J.M., Soria, D., Green, A., Ellis, I.O., Zou, W. and Qiu, G. 2019. An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA. IEEE Transactions on Medical Imaging. 38 (2), pp. 617-628. https://doi.org/10.1109/TMI.2018.2868333
| Title | An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA |
|---|---|
| Type | Journal article |
| Authors | Liu, J. |
| Xu, B. | |
| Zheng, C. | |
| Gong, Y. | |
| Garibaldi, J.M. | |
| Soria, D. | |
| Green, A. | |
| Ellis, I.O. | |
| Zou, W. | |
| Qiu, G. | |
| Abstract | One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system which directly predicts the H-Score automatically. Our system imitates the pathologists’ decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumour and non-tumour), a second FCN to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two FCNs and the stain intensity description image as input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a |
| Keywords | H-Score, Immunohistochemistry, Diaminobenzidine, Convolutional Neural Network, Breast Cancer |
| Journal | IEEE Transactions on Medical Imaging |
| Journal citation | 38 (2), pp. 617-628 |
| ISSN | 0278-0062 |
| Year | 2019 |
| Publisher | IEEE |
| Accepted author manuscript | |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/TMI.2018.2868333 |
| Publication dates | |
| Published online | 03 Sep 2018 |
| Published in print | Feb 2019 |
| Funder | University of Nottingham |