Name
Abstract | ||
|---|---|---|
High resolution histology images contain information related to disease prognosis. However, survival prediction based on current clinical grading systems, which rely heavily on a pathologist's histological assessment, has significant limitations due to the heterogeneity and complexity of tissue phenotypes. To address these challenges, we propose a deep learning framework that leverages hierarchical graph-based representations to enable more precise prediction of progression-free survival in prostate cancer patients. Unlike conventional approaches that analyze patch-based or cell-based pathomic features alone without considering their spatial connectivity, we explore multi-scale topological structures of whole slide images in an integrative context. Extensive experiments have demonstrated the effectiveness of our model for better progression prediction. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-87237-3_22 | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VIII |
Keywords | DocType | Volume |
Progression free survival, Graph convolutional neural network, Self-supervised learning, Hierarchical graph representations | Conference | 12908 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zichen Wang | 1 | 4 | 1.75 |
| Jiayun Li | 2 | 10 | 4.65 |
| Zhufeng Pan | 3 | 0 | 0.34 |
| Wenyuan Li | 4 | 6 | 4.22 |
| Anthony E. Sisk | 5 | 0 | 0.34 |
| Huihui Ye | 6 | 0 | 0.34 |
| William Speier | 7 | 2 | 2.06 |
| Corey W. Arnold | 8 | 9 | 3.56 |