Transfer learning recently became a popular technique for training machine learning algorithms. The goal is to transfer some information from dataset A (the source) to dataset B (the target). This increases the total amount of data the classifier learns from, leading to a more robust algorithm. This is very important for medical imaging datasets, which […]
The project addresses melanoma classification in skin lesion images. Typically machine learning algorithms for this application would learn from images which have been labeled as melanoma or not. A less explored option is to learn from relative assessments of images, for example, whether images are similar to each other or not. Such assessments can be […]
A PhD position is available at UMC Utrecht, in collaboration with our group. For more information and applying, please visit the UMC Utrecht website.
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 […]
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 […]
Welcome to the IMAG/e website! The website is still under construction, but stay tuned for more updates in the 2017-2018 academic year.