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Weakly Supervised Gleason Grading of Prostate Cancer Slides using Graph Neural Network
Topics: Classification and Clustering; Deep Learning and Neural Networks; Image and Video Analysis and Understanding; Medical Imaging
Nan Jiang 1 ; Yaqing Hou 1 ; Dongsheng Zhou 2 ; Pengfei Wang 1 ; Jianxin Zhang 3 and Qiang Zhang 1
Affiliations: 1 School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China ; 2 Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China ; 3 School of Computer Science and Engineering, Dalian Minzu University, Dalian 116600, China
Keyword(s): Prostate Cancer, Gleason Grading, Graph Neural Network, Weakly Supervised.
Abstract: Gleason grading of histopathology slides has been the “gold standard” for diagnosis, treatment and prognosis of prostate cancer. For the heterogenous Gleason score 7, patients with Gleason score 3+4 and 4+3 show a significant statistical difference in cancer recurrence and survival outcomes. Considering patients with Gleason score 7 reach up to 40% among all prostate cancers diagnosed, the question of choosing appropriate treatment and management strategy for these people is of utmost importance. In this paper, we present a Graph Neural Network (GNN) based weakly supervised framework for the classification of Gleason score 7. First, we construct the slides as graphs to capture both local relations among patches and global topological information of the whole slides. Then GNN based models are trained for the classification of heterogeneous Gleason score 7. According to the results, our approach obtains the best performance among existing works, with an accuracy of 79.5% on TCGA datase t. The experimental results thus demonstrate the significance of our proposed method in performing the Gleason grading task. (More)
Gleason grading of histopathology slides has been the “gold standard” for diagnosis, treatment and prognosis of prostate cancer. For the heterogenous Gleason score 7, patients with Gleason score 3+4 and 4+3 show a significant statistical difference in cancer recurrence and survival outcomes. Considering patients with Gleason score 7 reach up to 40% among all prostate cancers diagnosed, the question of choosing appropriate treatment and management strategy for these people is of utmost importance. In this paper, we present a Graph Neural Network (GNN) based weakly supervised framework for the classification of Gleason score 7. First, we construct the slides as graphs to capture both local relations among patches and global topological information of the whole slides. Then GNN based models are trained for the classification of heterogeneous Gleason score 7. According to the results, our approach obtains the best performance among existing works, with an accuracy of 79.5% on TCGA dataset. The experimental results thus demonstrate the significance of our proposed method in performing the Gleason grading task.
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Paper citation in several formats:
Jiang, N., Hou, Y., Zhou, D., Wang, P., Zhang, J. and Zhang, Q. (2021). Weakly Supervised Gleason Grading of Prostate Cancer Slides using Graph Neural Network. 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 426-434. DOI: 10.5220/0010264804260434
@conference{icpram21,
author={Nan Jiang and Yaqing Hou and Dongsheng Zhou and Pengfei Wang and Jianxin Zhang and Qiang Zhang},
title={Weakly Supervised Gleason Grading of Prostate Cancer Slides using Graph Neural Network},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2021},
pages={426-434},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010264804260434},
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 - Weakly Supervised Gleason Grading of Prostate Cancer Slides using Graph Neural Network
SN - 978-989-758-486-2
IS - 2184-4313
AU - Jiang, N.
AU - Hou, Y.
AU - Zhou, D.
AU - Wang, P.
AU - Zhang, J.
AU - Zhang, Q.
PY - 2021
SP - 426
EP - 434
DO - 10.5220/0010264804260434
PB - SciTePress