Object Detection Frameworks for Fully Automated Particle Picking in Cryo-EM

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dc.contributor.advisorBrubaker, Marcus
dc.contributor.authorMasoumzadeh Tork, Abbas
dc.date.accessioned2019-11-22T18:52:31Z
dc.date.available2019-11-22T18:52:31Z
dc.date.copyright2019-08
dc.date.issued2019-11-22
dc.date.updated2019-11-22T18:52:31Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractParticle picking in cryo-EM is a form of object detection for noisy, low contrast, and out-of-focus microscopy images, taken of different (unknown) structures. This thesis presents a fully automated approach which, for the first time, explicitly considers training on multiple structures, while simultaneously learning both specialized models for each structure used for training and a generic model that can be applied to unseen structures. The presented architecture is fully convolutional and divided into two parts: (i) a portion which shares its weights across all structures and (ii) N+1 parallel sets of sub-architectures, N of which are specialized to the structures used for training and a generic model whose weights are tied to the layers for the specialized models. Experiments reveal improvements in multiple use cases over the-state-of-art and present additional possibilities to practitioners.
dc.identifier.urihttp://hdl.handle.net/10315/36760
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsCryo-EM
dc.subject.keywordsParticle picking
dc.subject.keywordsObject detection
dc.subject.keywordsFully convolutional
dc.subject.keywordsDataset bias
dc.titleObject Detection Frameworks for Fully Automated Particle Picking in Cryo-EM
dc.typeElectronic Thesis or Dissertation

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