YorkSpace has migrated to a new version of its software. Access our Help Resources to learn how to use the refreshed site. Contact diginit@yorku.ca if you have any questions about the migration.
 

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

Simple item page
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

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Masoumzadeh-Tork_Abbas_2019_Masters.pdf
Size:
29.82 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
1.83 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
YorkU_ETDlicense.txt
Size:
3.38 KB
Format:
Plain Text
Description:
Download

Collections

Computer Science and Engineering

All items in the YorkSpace institutional repository are protected by copyright, with all rights reserved except where explicitly noted.