Meta-learning for medical image segmentation

Imagine you have experience with two segmentation applications (for example, tissue segmentation in brain MRI, and cell segmentation in histopathology), and you know that different (deep) learning methods work best in each application. Can you decide which method to use on a third application, for example segmentation of vessels in retinal images, without trying all […]

Read More

Weakly supervised learning in medical imaging (various projects)

Data is often only weakly annotated: for example, for a medical image, we might know the patient’s overall diagnosis, but not where the abnormalities are located, because obtaining ground-truth annotations is very time-consuming. Multiple instance learning (MIL) is an extension of supervised machine learning, aimed at dealing with such weakly labeled data. For example, a […]

Read More