Publications associées à Crowding asymmetries in a neural model of image segmentation

Publications associées (15)

Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual Metrics

Measuring perceptual similarity is a key tool in computer vision. In recent years perceptual metrics based on features extracted from neural networks with large and diverse training sets, e.g. CLIP, have become popular. At the same time, the metrics extrac ...

IEEE2025

Meteorological information prediction using images from webcams

Demetri Psaltis, Christophe Moser, Luisa Lambertini, Roy Sarkis

One aspect of the present invention proposes a method of predicting meteorological information by using an artificial deep neural network. The present invention according to one example focuses on image processing, specifically on fusing a plurality of ima ...

2025

Pattern Formation in a Spiking Neural-Field of Renewal Neurons

Grégory Didier Dumont

Elucidating the neurophysiological mechanisms underlying neural pattern formation remains an outstanding challenge in Computational Neuroscience. In this paper, we address the issue of understanding the emergence of neural patterns by considering a network ...

2024

Cross-Age and Cross-Site Domain Shift Impacts on Deep Learning-Based White Matter Fiber Estimation in Newborn and Baby Brains

Jean-Philippe Thiran, Meritxell Bach Cuadra, Rizhong Lin

Deep learning models have shown great promise in estimating tissue microstructure from limited diffusion magnetic resonance imaging data. However, these models face domain shift challenges when test and train data are from different scanners and protocols, ...

IEEE2024

Machine Understanding of Architectural Space: From Analytical to Generative Applications

Mikhael Johanes

The unprecedented development of machine learning (ML) and artificial intelligence (AI) has opened new ways of capturing architectural quality, where large neural networks have demonstrated remarkable capabilities compared to traditional rule-based approac ...

EPFL2024

Advancing Thalamic Nuclei Segmentation: The Impact of Compressed Sensing on MRI Processing

Tobias Kober, Tom Hilbert

The thalamus is a collection of gray matter nuclei that play a crucial role in sensorimotor processing and modulation of cortical activity. Characterizing thalamic nuclei non-invasively with structural MRI is particularly relevant for patient populations w ...

Wiley2024

Machine learning and its impact on psychiatric nosology: Findings from a qualitative study among German and Swiss experts

Georg Michael Joachim Starke

The increasing integration of Machine Learning (ML) techniques into clinical care, driven in particular by Deep Learning (DL) using Artificial Neural Nets (ANNs), promises to reshape medical practice on various levels and across multiple medical fields. Mu ...

2023

ShapeNet: Shape constraint for galaxy image deconvolution

Utsav Akhaury

Deep learning (DL) has shown remarkable results in solving inverse problems in various domains. In particular, the Tikhonet approach is very powerful in deconvolving optical astronomical images. However, this approach only uses the l(2) loss, which does no ...

2022

what If...? Pandemic Policy-decision-support to Guide a Cost-benefit-optimised, Country- Specific Response

Francesco Posa, Lucas Christophe Massemin, Giorgio Mannarini, Martin Jaggi, Mary-Anne Hartley, Tatjana Chavdarova, Prakhar Gupta, Thierry Bossy

Background After 18 months of responding to the COVID-19 pandemic, there is still no agreement on the optimal combination of mitigation strategies. The efficacy and collateral damage of pandemic policies are dependent on constantly evolving viral epidemiol ...

PUBLIC LIBRARY SCIENCE2022

Bayesian Skip Net: Building on Prior Information for the Prediction and Segmentation of Stroke Lesions

Dimitri Nestor Alice Van De Ville, Maria Giulia Preti

Perfusion CT is widely used in acute ischemic stroke to determine eligibility for acute treatment, by defining an ischemic core and penumbra. In this work, we propose a novel way of building on prior information for the automatic prediction and segmentatio ...

SPRINGER INTERNATIONAL PUBLISHING AG2021

Data driven approximation of parametrized PDEs by reduced basis and neural networks

Simone Deparis, Luca Pegolotti, Niccolò Dal Santo

ACADEMIC PRESS INC ELSEVIER SCIENCE2020

A phase-change memory model for neuromorphic computing

Irem Boybat Kara, Abu Sebastian

Phase-change memory (PCM) is an emerging non-volatile memory technology that is based on the reversible and rapid phase transition between the amorphous and crystalline phases of certain phase-change materials. The ability to alter the conductance levels i ...

AMER INST PHYSICS2018

Influence of Saliency and Social Impairments on the Development of Intention Recognition

Aude Billard, Laura Bénédicte Marine Cohen

Among the symptoms of schizophrenia, deficits in the recognition of intention is one of the most studied. However, there is no cognitive model of intention recognition that takes into account both innate and environmental/developmental factors. This work p ...

Springer Int Publishing Ag2016

From Genes to Organisms : Bioinformatics System Models and Software

Thomas Schaffter

The expression of genes is controlled by regulatory networks, which performspecific functions in a cell. Gene networks play a crucial role in the development of multicellular organisms by precisely coordinating spatial and temporal gene expression patterns ...

EPFL2014

Total activation: fMRI deconvolution through spatio-temporal regularization

Dimitri Nestor Alice Van De Ville, François Lazeyras, Fikret Isik Karahanoglu

Confirmatory approaches to fMRI data analysis look for evidence for the presence of pre-defined regressors modeling contributions to the voxel time series, including the BOLD response following neuronal activation. As more complicated questions arise about ...

Elsevier2013