Publications related to Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets

Three-dimensional evaluation of the transverse rotator cuff muscle's resultant force angle in relation to scapulohumeral subluxation and glenoid vault morphology in nonpathological shoulders

Alexandre Terrier, Alain Farron, Patrick Goetti, Matthieu Boubat

Background: Static posterior subluxation of the humeral head (SPSH) results in glenohumeral osteoarthritis. Treatment strategies for SPSH with or without resulting osteoarthritis remain challenging. There is growing interest in evaluating the rotator cuff ...

Mosby-Elsevier2024

Glenohumeral joint force prediction with deep learning

Dominique Pioletti, Alexandre Terrier, Patrick Goetti, Philippe Büchler

Deep learning models (DLM) are efficient replacements for computationally intensive optimization techniques. Musculoskeletal models (MSM) typically involve resource-intensive optimization processes for determining joint and muscle forces. Consequently, DLM ...

London2024

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Evann Pierre Guy Courdier

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power.Simultaneously, a critical pain point arises as several computer vision applications are deployed ...

EPFL2024

Incorporating Projective Geometry into Deep Learning

Michal Jan Tyszkiewicz

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

EPFL2024

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Devis Tuia, Gaston Jean Lenczner, Thiên-Anh Claris Nguyen, Marc Conrad Russwurm

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Elsevier Science Inc2024

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Jean-Paul Richard Kneib, Emma Elizabeth Tolley, Tianyue Chen, Michele Bianco

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Oxford Univ Press2024

Aggregating Spatial and Photometric Context for Photometric Stereo

David Honzátko

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

EPFL2024

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Seyed Mohammad Mahdi Johari

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

EPFL2024

Partial discharge localization in power transformer tanks using machine learning methods

Marcos Rubinstein, Hamidreza Karami

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets

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Three-dimensional evaluation of the transverse rotator cuff muscle's resultant force angle in relation to scapulohumeral subluxation and glenoid vault morphology in nonpathological shoulders

Glenohumeral joint force prediction with deep learning

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Incorporating Projective Geometry into Deep Learning

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Aggregating Spatial and Photometric Context for Photometric Stereo

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Partial discharge localization in power transformer tanks using machine learning methods

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Three-dimensional evaluation of the transverse rotator cuff muscle's resultant force angle in relation to scapulohumeral subluxation and glenoid vault morphology in nonpathological shoulders

Glenohumeral joint force prediction with deep learning

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Incorporating Projective Geometry into Deep Learning

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Aggregating Spatial and Photometric Context for Photometric Stereo

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Partial discharge localization in power transformer tanks using machine learning methods

Deep Learning Theory Through the Lens of Diagonal Linear Networks