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September 3, 2018

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 the possibilities first?

In machine learning this idea of predicting which methods will perform better on a given dataset is called “meta-learning”. This can be done by characterizing each (dataset, method) pair with several “meta-features”, which describe the data (for example, the number of images) and the method (for example, how many layers a neural network has). The label of this pair is the performance of the method on the dataset. This way, a meta-classifier can learn what type of data and classifiers perform well together.

An important open question is how to choose the meta-features for this problem. In this MSc project, you will investigate how to adapt meta-learning features from the literature to medical imaging problems, and engineer specialized features that might not be applicable to other types of data. You will work on a set of publicly available medical imaging datasets, and implement your methods in the OpenML platform.

Some experience with machine learning is required, experience with Python is preferred. Experience with medical imaging is preferred but not required.

Supervisors: Dr. Veronika Cheplygina and Dr. Joaquin Vanschoren (Data Mining, Department of Computer Science)

Contact: v.cheplygina at tue.nl

References

Cheplygina, V., Moeskops, P., Veta, M., Dashtbozorg, B., & Pluim, J. P. W. (2017). Exploring the similarity of medical imaging classification problems. In Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (pp. 59-66). Springer.

Vanschoren, J., Blockeel, H., Pfahringer, B., & Holmes, G. (2012). Experiment databases. Machine Learning87(2), 127-158.

 

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