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Deep Learning Tools for image classification in Cryo-electron microscopy

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

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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.

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Deep Learning Tools for image classification in Cryo-electron microscopy