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Real time parsimonious tumour tracking on X-ray images using reinforcement learning

Little, Nathan
(2021)

Files

Little_24801800_2021.pdf
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Details

Supervisors
Macq, Benoît
Faculty
Ecole polytechnique de Louvain
Degree label
Master [120] : ingénieur civil en informatique, à finalité spécialisée
Abstract
The goal of this thesis is to provide a tool that uses a reinforcement learning agent with a tracker to optimise, in our case, the image acquisition scheduling of X-ray images for a real-time radiotherapy treatment given a maximal number of images that can be taken. The agent is then tested with three different trackers: a Kalman filter, a Particle filter and an RNN.

Citations

  • APA

Little, N. (2021). Real time parsimonious tumour tracking on X-ray images using reinforcement learning. https://dial-mem.test.bib.ucl.ac.be/handle/2078.2/24143