Lymphocyte Detection in Hematoxylin-Eosin Stained Histopathological Images of Breast Cancer

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dc.contributor.advisorSadakata, Makiko
dc.contributor.advisorCiompi, Francesco
dc.contributor.authorSonsma, Patrick
dc.date.issued2019-07-29
dc.description.abstractLymphocytes are immune cells that form an important bio-marker in the prognosis of breast cancer. In some cases more e ective treatment can be chosen based on the lymphocyte presence near tumor regions. For trained pathologists the detection of lymphocytes in Hematoxylin-Eosin stained images is however a challenging and time intensive task with subjective interpretations. In this research we explore the lymphocyte detection problem with a deep learning approach and strive towards a robust, objective and e cient tool for computer aided diagnosis. We generate a large data-set with machine produced labels by applying an existing model on destained and restained immunohistochemical histopathological images. On this data we train and evaluate a more minimal rendition of the known YOLO object detection model and report moderate results.en_US
dc.embargo.lift10000-01-01
dc.embargo.typePermanent embargoen_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/10331
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationMaster Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeMasteren_US
dc.titleLymphocyte Detection in Hematoxylin-Eosin Stained Histopathological Images of Breast Canceren_US
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