Domain-Specific Language for Transfer Learning / Uncertainty Quantification

Recent breakthroughs in biomedical image analysis have been based on the application of advanced machine learning for large imaging data sets. For instance, convolutional neural network-based classifiers have recently been shown to reach or even exceed human-level performance in a range of disease- and abnormality detection tasks, based on tens of thousands of images. However, such machine learning approaches often fail when confronted with changes to the input data, e.g. images from a different MRI machine, or patients of a different age group or ethnicity.