Abstract
Graphs are useful in analysing histopathological images as they are able to represent neighbourhood interactions and spatial relationships. Typically graph nodes represent cells and the vertices are constructed by applying a nearest neighbor algorithm to cell's locations. When passing these graphs through one graph neural network (GNN) message passing step, each node can only utilise features from nodes within its immediate neighbourhood to make a classification. To overcome this, we introduce two levels of hierarchically connected nodes that we term "supernodes". These supernodes, used in conjunction with at least four GNN message passing steps, allow for cell node classifications to be influenced by a wider area, enabling the entire graph to learn tissue-level structures. The method is evaluated on a supervised task to classify individual cells as belonging to a specific tissue class. Results demonstrate that the inclusion of supernodes with multiple GNN message passing steps increases model accuracy.
Original language | English |
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Title of host publication | IMAGING SYSTEMS FOR GI ENDOSCOPY, AND GRAPHS IN BIOMEDICAL IMAGE ANALYSIS, ISGIE 2022 |
Editors | L Manfredi, S Ahmadi, M Bronstein, A Kazi, D Lomanto, A Mathew, L Magerand, K Mullakaeva, B Papiez, RH Taylor, E Trucco |
Publisher | Springer International Publishing AG |
Pages | 99-107 |
Number of pages | 9 |
Volume | 13754 |
ISBN (Electronic) | 978-3-031-21083-9 |
ISBN (Print) | 9783031210822 |
DOIs | |
Publication status | Published - 2022 |
Event | MICCAI 2022 Imaging Systems for GI Endoscopy (ISGIE) - Singapore, Singapore Duration: 18 Sept 2022 → … https://miccai2022-isgie.github.io/ |
Publication series
Series | Lecture Notes in Computer Science |
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Volume | 13754 |
ISSN | 0302-9743 |
Workshop
Workshop | MICCAI 2022 Imaging Systems for GI Endoscopy (ISGIE) |
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Abbreviated title | MICCAI 2022 |
Country/Territory | Singapore |
City | Singapore |
Period | 18/09/22 → … |
Internet address |
Keywords
- Graph neural network
- Node classification
- Digital pathology
- HETEROGENEITY