Search Results for author: Olivier Salvado

Found 9 papers, 2 papers with code

PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels

no code implementations7 Dec 2022 Huaxi Huang, Hui Kang, Sheng Liu, Olivier Salvado, Thierry Rakotoarivelo, Dadong Wang, Tongliang Liu

The early stopping strategy averts updating CNNs during the early training phase and is widely employed in the presence of noisy labels.

Learning with noisy labels

CorticalFlow$^{++}$: Boosting Cortical Surface Reconstruction Accuracy, Regularity, and Interoperability

no code implementations14 Jun 2022 Rodrigo Santa Cruz, Léo Lebrat, Darren Fu, Pierrick Bourgeat, Jurgen Fripp, Clinton Fookes, Olivier Salvado

Using the state-of-the-art CorticalFlow model as a blueprint, this paper proposes three modifications to improve its accuracy and interoperability with existing surface analysis tools, while not sacrificing its fast inference time and low GPU memory consumption.

Surface Reconstruction

CorticalFlow: A Diffeomorphic Mesh Deformation Module for Cortical Surface Reconstruction

no code implementations6 Jun 2022 Léo Lebrat, Rodrigo Santa Cruz, Frédéric de Gournay, Darren Fu, Pierrick Bourgeat, Jurgen Fripp, Clinton Fookes, Olivier Salvado

In this paper we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensional image, learns to deform a reference template towards a targeted object.

Surface Reconstruction

Learning Dense Correspondence from Synthetic Environments

no code implementations24 Mar 2022 Mithun Lal, Anthony Paproki, Nariman Habili, Lars Petersson, Olivier Salvado, Clinton Fookes

Results show that training 2D-3D mapping network models on synthetic data is a viable alternative to using real data.

CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction

1 code implementation NeurIPS 2021 Leo Lebrat, Rodrigo Santa Cruz, Frederic de Gournay, Darren Fu, Pierrick Bourgeat, Jurgen Fripp, Clinton Fookes, Olivier Salvado

In this paper, we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensional image, learns to deform a reference template towards a targeted object.

Surface Reconstruction

MongeNet: Efficient Sampler for Geometric Deep Learning

1 code implementation CVPR 2021 Léo Lebrat, Rodrigo Santa Cruz, Clinton Fookes, Olivier Salvado

Recent advances in geometric deep-learning introduce complex computational challenges for evaluating the distance between meshes.

DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction

no code implementations22 Oct 2020 Rodrigo Santa Cruz, Leo Lebrat, Pierrick Bourgeat, Clinton Fookes, Jurgen Fripp, Olivier Salvado

Having these limitations in mind, we propose DeepCSR, a 3D deep learning framework for cortical surface reconstruction from MRI.

Surface Reconstruction

A Multiple Decoder CNN for Inverse Consistent 3D Image Registration

no code implementations15 Feb 2020 Abdullah Nazib, Clinton Fookes, Olivier Salvado, Dimitri Perrin

The recent application of deep learning technologies in medical image registration has exponentially decreased the registration time and gradually increased registration accuracy when compared to their traditional counterparts.

Image Registration Medical Image Registration

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