In recent years, we have seen a rapid increase in the number of deep learning algorithms in the medical domain that for a given task surpass the performance of trained professionals. Such deep learning algorithms are supervised using large carefully annotated datasets. In the medical domain it is not really possible to make such large datasets for all kinds of diseases. The problem of diagnosing a wide range of diseases in these resource constrained environments is to use some form of unsupervised anomaly detection.
Unsupervised Anomaly Detection for disease requires estimating healthy anatomy normative distribution from a large set of healthy examples and to detect outliers at inference time from either a learned feature space or from high reconstruction loss.
A single imaging sequence will be selected (i.e. FLAIR). Current state of the art methods rely on autoencoder reconstruction based pipelines, however clinical success is limited here and this project would focus on supplementing and/or improving this approach through novelty.
|Title:||Diverse Anomaly Detection Projects @deepc|
|Area:||Machine Learning, Medical Imaging, Computer Vision|