Predicting Position and Volume of Hemorrhagic Strokes
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Examensarbete för masterexamen
Model builders
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Abstract
This study has explored different neural network methods for position and volume
prediction of hemorrhagic strokes. Three different data sets of microwave data were
used as input for the different networks. A simple diagnostic classifier was used as
benchmark to help in evaluating success. The two largest challenges of the study
was instrument variations in the data as well as the limited data available. A Multi-
Source Adversarial Domain Adaptation (MSADA) network was introduced to lower
the effect of the instrument variations, and a Divergence Based Domain Adaptation
(DBDA) network was implemented to attempt to resolve the limited number of data
samples.
The networks showed promising results for both position and volume in all three
data sets used. The MSADA network successfully lowered the instrument specific
noise when predicting volumes, but was concluded to be unnecessary for position
classification. The DBDA network was not enough to remedy the lack of sufficient
data.
Description
Keywords
Deep neural networks, machine learning, adversarial domain adaptation, multi-source adaptation, microwave data, stroke detection