Weakly Supervised Gleason Grading of Prostate Cancer Slides using Graph Neural Network

Nan Jiang, Yaqing Hou, Dongsheng Zhou, Pengfei Wang, Jianxin Zhang, Qiang Zhang

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 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 Harvard Style

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 - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 426-434. DOI: 10.5220/0010264804260434


in Bibtex Style

@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 - Volume 1: ICPRAM,},
year={2021},
pages={426-434},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010264804260434},
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 - Weakly Supervised Gleason Grading of Prostate Cancer Slides using Graph Neural Network
SN - 978-989-758-486-2
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