Search Results for author:
Found 25 papers, 3 papers with code
Second Order Kinematic Surface Fitting in Anatomical Structures
The application of kinematic surface fitting, a method for characterizing shapes through parametric stationary velocity fields, has shown promising results in computer vision and computer-aided design.
Morphologically-Aware Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO)
We compared extensively our method on several datasets with the STAPLE method and the naive segmentation averaging method, showing that it leads to binary consensus masks of intermediate size between Majority Voting and STAPLE and to different posterior probabilities than Mask Averaging and STAPLE methods.
Zero-shot-Learning Cross-Modality Data Translation Through Mutual Information Guided Stochastic Diffusion
This paper proposes a new unsupervised zero-shot-learning method named Mutual Information guided Diffusion cross-modality data translation Model (MIDiffusion), which learns to translate the unseen source data to the target domain.
Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes?
Instead, only a secured remote access to a data lake is granted to the model owner without any ability to retrieve data from the data lake.
Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning
The resulting image shows an order-of-magnitude gain in axial resolution compared to a delay-and-sum reconstruction with unprocessed element data.
Geodesic squared exponential kernel for non-rigid shape registration
This work addresses the problem of non-rigid registration of 3D scans, which is at the core of shape modeling techniques.
Attention-based Multiple Instance Learning with Mixed Supervision on the Camelyon16 Dataset
Since the standardization of Whole Slide Images (WSIs) digitization, the use of deep learning methods for the analysis of histological images has shown much potential.
Attention for Image Registration (AiR): an unsupervised Transformer approach
To further advance learning approaches in image registration, we introduce an attention mechanism in the deformable image registration problem.
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix
We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration.
Long Short-Term Memory Neuron Equalizer
In this work we propose a neuromorphic hardware based signal equalizer by based on the deep learning implementation.
Quasi-symplectic Langevin Variational Autoencoder
The HVAE adapted the Hamiltonian dynamic flow into variational inference that significantly improves the performance of the posterior estimation.
A Deep Learning based Fast Signed Distance Map Generation
Signed distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning.
Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI
We propose to learn a probabilistic motion model from a sequence of images.
Anatomically Consistent Segmentation of Organs at Risk in MRI with Convolutional Neural Networks
We report cross-validated quantitative results on a database of 44 contrast-enhanced T1-weighted MRIs with provided segmentations of the considered organs at risk, which were originally used for radiotherapy planning.
Robust Cochlear Modiolar Axis Detection in CT
Herein, we present an algorithm for robust modiolar axis detection in clinical CT images.
A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments
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.
Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank
First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion.
Learning a Probabilistic Model for Diffeomorphic Registration
Besides, we visualized the learned latent space and show that the encoded deformations can be used to transport deformations and to cluster diseases with a classification accuracy of 83% after applying a linear projection.
Ranked #1 on
Diffeomorphic Medical Image Registration
on Automatic Cardiac Diagnosis Challenge (ACDC)
(using extra training data)
Deformable Medical Image Registration
Diffeomorphic Medical Image Registration
+1
Deep Learning with Mixed Supervision for Brain Tumor Segmentation
In this paper, we propose to use both types of training data (fully-annotated and weakly-annotated) to train a deep learning model for segmentation.
Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Loss
In this preliminary study, we propose automated, two-stage, three-dimensional U-Nets with convolutional neural network, for the challenging task of left atrial segmentation.
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow
We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart.
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context
Furthermore, we propose a network architecture in which the different MR sequences are processed by separate subnetworks in order to be more robust to the problem of missing MR sequences.
3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation
We propose a method based on deep learning to perform cardiac segmentation on short axis MRI image stacks iteratively from the top slice (around the base) to the bottom slice (around the apex).
Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration
This model enables to also generate normal or pathological deformations of any new image based on the probabilistic latent space.
3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation
We present a novel automated method to segment the myocardium of both left and right ventricles in MRI volumes.
