Document type
Bachelor thesisPublication date
Publication license
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/223100
Deep Learning Tools for image classification in Cryo-electron microscopy
Journal Title
Authors
Director/Tutor
Journal ISSN
Volume Title
Related resource
Abstract
Cryo-electron microscopy is an imaging technique used for 3D reconstruction of biomolecules, enabling researchers to study their structures. However, due to low signal-to-noise ratios in captured images, 2D classification is a critical preprocessing step. This thesis explores the application of a deep learning approach, specifically a similarity network, to address this challenge.
A Siamese model, trained with a Triplet Loss function, is used to differentiate between similar and dissimilar images. The model was trained on a dataset with known ground truth and tested on two types of unseen data: a similar dataset with ground truth and a different dataset without the ground truth. This study demonstrates the potential of deep learning to complement traditional 2D classification methods in cryo-EM.
Description
Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2025, Tutor: David Maluenda
Subject (English)
Citation
Collections
Citation
LORENZANA SANTUYO, Joshua. Deep Learning Tools for image classification in Cryo-electron microscopy. [consulted: 8 of June of 2026]. Available at: https://hdl.handle.net/2445/223100