TIA Centre: Demos
We invite you to explore interactive visualisations of the computational models developed by the TIA Centre, showcasing a range of applications of these state-of-the-art models. These tools are designed to complement our published research by offering an accessible, hands-on way to examine the models’ outputs in greater detail. To provide some context – these visualisations help bridge the gap between complex technical work and practical understanding, making it easier for diverse audiences to engage with the findings.
Clicking on each visualisation will take you to a dedicated demo page, where you can find additional details, background information, and user guidance. Videos for the demos will be coming soon.
IGUANA: Interpretable gland graphs for colon screening
IGUANA presents a graph representations of colon biopsy tissue slides for screening purposes by using interpretable gland-graphs that reflect diagnostic features. This approach increases model explainability and helps improve the confidence of athologists in automated diagnoses. Using graphs with meticulously-defined input features provides highly interpretable explanations, which is particularly important in medicine to ensure algorithm fairness and identify potential bias in training data.
MesoGraph: Graph networks for mesothelioma subtyping
Publication, Source Code, Demo
This paper proposes a model using a dual-task Graph Neural Network (GNN) architecture with ranking loss to accurately diagnose morphological subtypes of malignant mesothelioma. The approach scores tissue down to cellular resolution to allow quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score of all the cells in the sample, and validates the model predictions through an analysis of the typical morphological features of cells according to their predicted score. The proposed approach has predictive performance and ultimately improves treatment decisions for patients with mesothelioma.
Social-IDaRS: Enhanced deep learning model with social analysis of cell networks
Here, a new approach to enhance deep learning-based methods for predicting molecular pathways and mutations in colorectal cancer is proposed. The approach involves incorporating cell interaction information using cell graphs and Social Network Analysis measures, which are computationally efficient and scalable. The approach demonstrated improved performance for several prediction tasks and provides insights into the correlation between cell interactions and molecular pathways/mutations.
HiGGsXplore: Histology gene groups explorer
Publication, Source Code, Demo
Identification of gene expression state of a cancer patient from routine pathology imaging and characterization of its phenotypic effects have significant clinical and therapeutic implications. However, prediction of expression of individual genes from whole slide images (WSIs) is challenging due to co-dependent or correlated expression of multiple genes. Here, we use a purely data-driven approach to first identify groups of genes with co-dependent expression and then predict their status from (WSIs) using a bespoke graph neural network. These gene groups allow us to capture the gene expression state of a patient with a small number of binary variables that are biologically meaningful and carry histopathological insights for clinically and therapeutic use cases. Prediction of gene expression state based on these gene groups allows associating histological phenotypes (cellular composition, mitotic counts, grading, etc.) with underlying gene expression patterns and opens avenues for gaining significant biological insights from routine pathology imaging directly.
ODYN: AI-based Prediction of Malignant Transformation in Oral Epithelial Dysplasia
Publication, Source Code, Demo
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity that have an increased risk of progression to malignancy. We developed a novel transformer-based pipeline, called the Oral Dysplasia Network (ODYN). ODYN can both classify OED and assign a predictive score (ODYN-score) to quantify the risk of malignant transformation, in haematoxylin and eosin (H&E) stained whole slide images (WSIs). Our pipeline outperformed other state-of-the-art methods, and gained comparable results to clinical grading systems, demonstrating the promise of computational pathology for the automatic detection, diagnosis and prognosis of OED.
