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 classifier trained on healthy and abnormal images, would be able to label a both a previously unseen image AND local patches in that image.
Figure 1: Supervised learning and multiple instance learning, shown for the task of detecting abnormalities in chest CT images. Images from Cheplygina, V., de Bruijne, M., & Pluim, J. P. W. (2018). Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. arXiv preprint arXiv:1804.06353.
There are still a number of open research directions. For example,
- How can we evaluate the patch-level predictions without ground-truth labels?
- Could we improve MIL algorithms by asking experts only a few questions, where they verify the algorithm’s decisions?
- What can we learn about MIL in medical imaging from other applications where it has been applied?
As a MSc student you would choose one or more medical imaging applications you are interested in, using an open dataset or a dataset available through collaborators, and work with us on formulating your own research question. Participating in a machine learning competition, creating open source tools and/or writing a paper for a scientific conference are also encouraged.
Some experience with machine learning is required (for example 8DC00 if you are a TU/e student). Experience with Python is preferred but experience with another programming language and willingness to learn Python is also sufficient.
Supervisor TU/e: Dr. Veronika Cheplygina (v.cheplygina at tue.nl)