250 citations · 462 across the 30 of their papers we have counts for
33 papers
High-Capacity Robust Medical Image Exfiltration via Neural Network Weight Replacement
Elie Thellier, Huiyu Li, Nicholas Ayache +1
Collaborative medical AI platforms allow researchers to train models on sensitive imaging data while restricting data export. However, trained models can serve as covert carriers o…
Data Exfiltration by Compression Attack: Definition and Evaluation on Medical Image Data
Huiyu Li, Nicholas Ayache, Hervé Delingette
With the rapid expansion of data lakes storing health data and hosting AI algorithms, a prominent concern arises: how safe is it to export machine learning models from these data l…
Mitigating Data Exfiltration Attacks through Layer-Wise Learning Rate Decay Fine-Tuning
Elie Thellier, Huiyu Li, Nicholas Ayache +1
Data lakes enable the training of powerful machine learning models on sensitive, high-value medical datasets, but also introduce serious privacy risks due to potential leakage of p…
Spatial regularisation for improved accuracy and interpretability in keypoint-based registration
Benjamin Billot, Ramya Muthukrishnan, Esra Abaci-Turk +4
Unsupervised registration strategies bypass requirements in ground truth transforms or segmentations by optimising similarity metrics between fixed and moved volumes. Among these m…
Generative Medical Image Anonymization Based on Latent Code Projection and Optimization
Huiyu Li, Nicholas Ayache, Hervé Delingette
Medical image anonymization aims to protect patient privacy by removing identifying information, while preserving the data utility to solve downstream tasks. In this paper, we addr…
Diffusion based Zero-shot Medical Image-to-Image Translation for Cross Modality Segmentation
Zihao Wang, Yingyu Yang, Yuzhou Chen +4
Cross-modality image segmentation aims to segment the target modalities using a method designed in the source modality. Deep generative models can translate the target modality ima…
Morphologically-Aware Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO)
Dimitri Hamzaoui, Sarah Montagne, Raphaële Renard-Penna +2
The extraction of consensus segmentations from several binary or probabilistic masks is important to solve various tasks such as the analysis of inter-rater variability or the fusi…
Zero-shot-Learning Cross-Modality Data Translation Through Mutual Information Guided Stochastic Diffusion
Zihao Wang, Yingyu Yang, Maxime Sermesant +2
Cross-modality data translation has attracted great interest in image computing. Deep generative models (\textit{e.g.}, GANs) show performance improvement in tackling those problem…
Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers
Zihao Wang, Yingyu Yang, Maxime Sermesant +1
Image registration is an essential but challenging task in medical image computing, especially for echocardiography, where the anatomical structures are relatively noisy compared t…
Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes?
Huiyu Li, Nicholas Ayache, Hervé Delingette
In privacy-preserving machine learning, it is common that the owner of the learned model does not have any physical access to the data. Instead, only a secured remote access to a d…
Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning
Nathan Blanken, Jelmer M. Wolterink, Hervé Delingette +3
Recently, super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) has received much attention. However, ULM relies on low concentrations of microbubbles i…
Geodesic squared exponential kernel for non-rigid shape registration
Florent Jousse, Xavier Pennec, Hervé Delingette +1
This work addresses the problem of non-rigid registration of 3D scans, which is at the core of shape modeling techniques. Firstly, we propose a new kernel based on geodesic distanc…
Bayesian Logistic Shape Model Inference: application to cochlea image segmentation
Wang Zihao, Demarcy Thomas, Vandersteen Clair +4
Incorporating shape information is essential for the delineation of many organs and anatomical structures in medical images. While previous work has mainly focused on parametric sp…
Attention for Image Registration (AiR): an unsupervised Transformer approach
Zihao Wang, Hervé Delingette
Image registration is a crucial task in signal processing, but it often encounters issues with stability and efficiency. Non-learning registration approaches rely on optimizing sim…
Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks
Wang Zihao, Vandersteen Clair, Demarcy Thomas +4
Metal Artifacts creates often difficulties for a high quality visual assessment of post-operative imaging in {c}omputed {t}omography (CT). A vast body of methods have been proposed…
Combining Bayesian and Deep Learning Methods for the Delineation of the Fan in Ultrasound Images
Hind Dadoun, Hervé Delingette, Anne-Laure Rousseau +2
Ultrasound (US) images usually contain identifying information outside the ultrasound fan area and manual annotations placed by the sonographers during exams. For those images to b…
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix
Julian Krebs, Hervé Delingette, Nicholas Ayache +1
We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the…
Long Short-Term Memory Neuron Equalizer
Zihao Wang, Zhifei Xu, Jiayi He +3
In this work we propose a neuromorphic hardware based signal equalizer by based on the deep learning implementation. The proposed neural equalizer is plasticity trainable equalizer…
Quasi-symplectic Langevin Variational Autoencoder
Zihao Wang, Hervé Delingette
Variational autoencoder (VAE) is a very popular and well-investigated generative model in neural learning research. To leverage VAE in practical tasks dealing with a massive datase…
A Deep Learning based Fast Signed Distance Map Generation
Zihao Wang, Clair Vandersteen, Thomas Demarcy +4
Signed distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. The computational complexity of SDM for 3D parametric shapes is oft…
Anatomically Consistent Segmentation of Organs at Risk in MRI with Convolutional Neural Networks
Pawel Mlynarski, Hervé Delingette, Hamza Alghamdi +2
Planning of radiotherapy involves accurate segmentation of a large number of organs at risk, i.e. organs for which irradiation doses should be minimized to avoid important side eff…
Robust Cochlear Modiolar Axis Detection in CT
Wilhelm Wimmer, Clair Vandersteen, Nicolas Guevara +2
The cochlea, the auditory part of the inner ear, is a spiral-shaped organ with large morphological variability. An individualized assessment of its shape is essential for clinical…
Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI
Julian Krebs, Tommaso Mansi, Nicholas Ayache +1
We propose to learn a probabilistic motion model from a sequence of images. Besides spatio-temporal registration, our method offers to predict motion from a limited number of frame…
A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments
Raphaël Sivera, Hervé Delingette, Marco Lorenzi +2
In this study we propose a deformation-based framework to jointly model the influence of aging and Alzheimer's disease (AD) on the brain morphological evolution. Our approach combi…