A graph neural network framework for mapping histological topology in oral mucosal tissue.
BMC Bioinformatics
; 23(1): 506, 2022 Nov 25.
Article
em En
| MEDLINE
| ID: mdl-36434526
ABSTRACT
BACKGROUND:
Histological feature representation is advantageous for computer aided diagnosis (CAD) and disease classification when using predictive techniques based on machine learning. Explicit feature representations in computer tissue models can assist explainability of machine learning predictions. Different approaches to feature representation within digital tissue images have been proposed. Cell-graphs have been demonstrated to provide precise and general constructs that can model both low- and high-level features. The basement membrane is high-level tissue architecture, and interactions across the basement membrane are involved in multiple disease processes. Thus, the basement membrane is an important histological feature to study from a cell-graph and machine learning perspective.RESULTS:
We present a two stage machine learning pipeline for generating a cell-graph from a digital H &E stained tissue image. Using a combination of convolutional neural networks for visual analysis and graph neural networks exploiting node and edge labels for topological analysis, the pipeline is shown to predict both low- and high-level histological features in oral mucosal tissue with good accuracy.CONCLUSIONS:
Convolutional and graph neural networks are complementary technologies for learning, representing and predicting local and global histological features employing node and edge labels. Their combination is potentially widely applicable in histopathology image analysis and can enhance explainability in CAD tools for disease prediction.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Aprendizado de Máquina
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article