Show simple item record

dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorEijkemans, M.J.C.
dc.contributor.authorHeijer, D. den
dc.date.accessioned2019-09-03T17:01:33Z
dc.date.available2019-09-03T17:01:33Z
dc.date.issued2019
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/33981
dc.description.abstractDecision support systems and automated classification are a vast area of research in the intersection of medicine and artificial intelligence. Significant accomplishments are delivered in the last decade, thanks to the revolution of computer vision. Automated classification of histopathological slides can potentially provide well-needed assistance to overworked pathologists, improve classification accuracy, and reduce inter and intra-observer error. Research proves that convolutional neural networks outperform pathologists on several tasks. However, these promising results are not yet integrated into the pathology department because of some intricate problems. The integration of deep learning algorithms can potentially enhance diagnostics and efficiency, which in turn makes healthcare more accessible and affordable.
dc.description.sponsorshipUtrecht University
dc.format.extent917600
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.titleMachine Learning as a classification tool for Histopathological analysi
dc.type.contentBachelor Thesis
dc.rights.accessrightsOpen Access
dc.subject.courseuuKunstmatige Intelligentie


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record