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Dmitrii Zhemchuzhnikov

Lundi 21 Octobre 2024

Deep Learning for 3D Spatial Data Analysis: Methods and Applications in the Fourier Domain

Abstract:
In the dynamically evolving field of deep learning for spatial data analysis, the exploration of three-dimensional (3D) data structures presents unique challenges and opportunities. This thesis centers on developing and evaluating methods for detecting arbitrary-shaped patterns in both regular and irregular volumetric data, focusing on Fourier domain-based approaches. The work addresses challenges of global and local rotation invariance, enabling effective learning of hierarchical spatial patterns and providing accurate interpretation of neural network operations.

A range of methods is presented, each targeting different aspects of 3D data analysis. Starting with a Fourier-based approach, the research evaluates the applicability of Fourier representations for both regular and irregular data. One technique, inspired by X-ray and cosmology domains, demonstrated strong performance in protein structure data. The thesis further introduces methods like ILPONet, which is invariant to local pattern orientations, and EquiLoPONet, a network providing analytical equivariance concerning the continuous rotational space. Additionally, a method based on point cloud convolutions explores the challenges and potential of processing irregular data without relying on Fourier transformations.

This research underscores both the strengths and limitations of Fourier domain-based approaches in 3D spatial data analysis, particularly their utility in handling regular data. The findings contribute valuable insights for future applications in fields like medical imaging and molecular data analysis, paving the way for more robust and versatile deep learning models.

Date et lieu

Lundi 21 Octobre 2024 à 15:30
Auditorium, Bâtiment IMAG
Zoom Meeting

Composition du jury

Sergei GRUDININ
Superviseur
Pablo CHACON
Senior Scientist, Institute of Physical Chemistry – Rapporteur
Risi KONDOR
Associate Professor, University of Chicago – Rapporteur
Elodie LAINE
Professeure des Universités, Sorbonne Université – Examinatrice
Valérie PERRIER
Professeure des Universités, Grenoble-INP UGA – Examinatrice
Sylvain MEIGNEN
Maître de Conférences, Grenoble INP – Invité
Sergi PUJADES
Maître de Conférences, Université de Grenoble Alpes – Invité

Publié le 10 octobre 2024

Mis à jour le 21 janvier 2025