| Application: | 12579, Machine Learning for Abnormality Detection in Imaging Studies with Application to Auto-QC and Identification of Pathology-specific Outliers through Correlation with Non-Image Data |
| Title: | Metric learning with spectral graph convolutions on brain connectivity networks |
| Size: | 864 B |
| Archived: | 26 Nov 2020 |
| Stability: | Complete |
| Personal: | No individual-level data |
Return 2879
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Notes
Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model structural or functional connectivity between a set of brain regions, graphs have proven to be of great importance. This is mainly due to the capability of revealing patterns related to brain development and disease, which were previously unknown. Evaluating similarity between these brain connectivity networks in a manner that accounts for the graph structure and is tailored for a particular application is, however, non-trivial. Most existing methods fail to accommodate the graph structure, discarding information that could be beneficial for further classification or regression analyses based on these similarities. We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s-GCN) in a supervised setting. The proposed framework takes into consideration the graph structure for the evaluation of similarity between a pair of graphs, by employing spectral graph convolutions that allow the generalisation of traditional convolutions to irregular graphs and operates in the graph spectral domain. We apply the proposed model on two datasets: the challenging ABIDE database, which comprises functional MRI data of 403 patients with autism spectrum disorder (ASD) and 468 healthy controls aggregated from multiple acquisition sites, and a set of 2500 subjects from UK Biobank. We demonstrate the performance of the method for the tasks of classification between matching and non-matching graphs, as well as individual subject classification and manifold learning, showing that it leads to significantly improved results compared to traditional methods.Application 12579
Machine Learning for Abnormality Detection in Imaging Studies with Application to Auto-QC and Identification of Pathology-specific Outliers through Correlation with Non-Image Data
The aim is to develop software for detection of abnormalities in images with an application to quality control and identification of correlations with non-image data. The core builds an abnormality detection system using advanced statistical machine learning. Abnormalities caused by data corruption and pathological processes such as cancer are detected by comparing test data to statistics of a normative model. Variations outside the norm are highlighted automatically. The software has potential to significantly improve the cost-effectiveness in imaging studies, ensure high data quality, support image-based diagnosis, and to find correlations with demographics, lifestyle, and Cancer Registry data. Automated QC is essential to guarantee high quality data in large-scale studies such as the Biobank Imaging Enhancement. The derived statistical models will further reveal clinically useful information about the population, can assist automated detection of pathologies such as cancer, and will allow to correlate image derived measurements with non-image data. This has the potential to facilitate further research in understanding demographic, genetic and lifestyle factors on pathological processes such as cancer, and the role of imaging biomarkers for early detection and risk prediction. We will develop software that allows to automatically process medical images and extract clinically useful information. We apply existing algorithms for the extraction of major organs and other regions of interest for the construction of statistical population models using advanced machine learning. The statistical models will be used to detect abnormalities in test images. Abnormalities, whether caused by poor data quality, corrupted data, or by pathological processes, will be detected as statistical outliers and can be reported to the human expert for further visual inspection. Further, we will perform statistical analyses to correlate image derived measurements with non-image data. Initially, 5,000 subjects.
| Lead investigator: | Dr Ben Glocker |
| Lead institution: | Imperial College London |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 2880 | Metric learning with spectral graph convolutions on brain connectivity networks | Sofia Ira Ktena (+6) | 2017 | NeuroImage |
Enabling scientific discoveries that improve human health