Miniatura

Fitxers

TFG-Lorenzana-Santuyo-Joshua.pdf (2.97 MB)

Tipus de document

Treball de fi de grau

Data de publicació

2025-01

Llicència de publicació

cc-by-nc-nd (c) Lorenzana, 2025
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/223100

Deep Learning Tools for image classification in Cryo-electron microscopy

Títol de la revista

Autors

Director/Tutor

ISSN de la revista

Títol del volum

Recurs relacionat

Resum

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.

Descripció

Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2025, Tutor: David Maluenda

Matèries

Aprenentatge profund, Xarxes neuronals, Treballs de fi de grau

Matèries (anglès)

Deep learning (Machine learning), Neural networks, Bachelor's theses

Citació

Col·leccions

Treballs Finals de Grau (TFG) - Física

Citació

LORENZANA SANTUYO, Joshua. Deep Learning Tools for image classification in Cryo-electron microscopy. [consulta: 22 de gener de 2026]. [Disponible a: https://hdl.handle.net/2445/223100]

Exportar metadades

JSON - METS

Compartir registre