Reconstructing simulated breast phantoms using neural networks inspired by the problem geometry

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dc.contributor Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta fi
dc.contributor University of Helsinki, Faculty of Science en
dc.contributor Helsingfors universitet, Matematisk-naturvetenskapliga fakulteten sv Enwald, Joel 2020
dc.identifier.uri URN:NBN:fi:hulib-202009304162
dc.description.abstract Mammography is used as an early detection system for breast cancer, which is one of the most common types of cancer, regardless of one’s sex. Mammography uses specialised X-ray machines to look into the breast tissue for possible tumours. Due to the machine’s set-up as well as to reduce the radiation patients are exposed to, the number of X-ray measurements collected is very restricted. Reconstructing the tissue from this limited information is referred to as limited angle tomography. This is a complex mathematical problem and ordinarily leads to poor reconstruction results. The aim of this work is to investigate how well a neural network whose structure utilizes pre-existing models and known geometry of the problem performs at this task. In this preliminary work, we demonstrate the results on simulated two-dimensional phantoms and discuss the extension of the results to 3-dimensional patient data. en
dc.language.iso eng
dc.publisher Helsingin yliopisto fi
dc.publisher University of Helsinki en
dc.publisher Helsingfors universitet sv
dc.subject neural networks
dc.subject deep learning
dc.subject breast cancer
dc.subject shift-and-add
dc.subject tomosynthesis
dc.subject convolution
dc.title Reconstructing simulated breast phantoms using neural networks inspired by the problem geometry en
dc.title.alternative Simuloitujen rintafantomien rekonstruointi ongelman geometrian inspiroimien neuroverkkojen avulla fi
dc.type.ontasot pro gradu -tutkielmat fi
dc.type.ontasot master's thesis en
dc.type.ontasot pro gradu-avhandlingar sv
dc.subject.discipline Matematiikka und
dct.identifier.urn URN:NBN:fi:hulib-202009304162

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