Search Results for author:
Found 22 papers, 5 papers with code
Ultrasound Imaging based on the Variance of a Diffusion Restoration Model
Despite today's prevalence of ultrasound imaging in medicine, ultrasound signal-to-noise ratio is still affected by several sources of noise and artefacts.
Muscle volume quantification: guiding transformers with anatomical priors
We propose a method for automatic segmentation of 18 muscles of the lower limb on 3D Magnetic Resonance Images to assist such morphometric analysis.
Diffusion Reconstruction of Ultrasound Images with Informative Uncertainty
Bringing the best from both worlds, we propose a hybrid approach leveraging advances in diffusion models.
Graph-based multimodal multi-lesion DLBCL treatment response prediction from PET images
Experimental results show that our proposed method outperforms classical supervised methods based on either clinical, imaging or both clinical and imaging data for the 2-year progression-free survival (PFS) classification accuracy.
Can ultrasound confidence maps predict sonographers' labeling variability?
Therefore, we propose to give the confidence maps as additional information to the networks.
Ultrasound Image Reconstruction with Denoising Diffusion Restoration Models
Ultrasound image reconstruction can be approximately cast as a linear inverse problem that has traditionally been solved with penalized optimization using the $l_1$ or $l_2$ norm, or wavelet-based terms.
Deformable image registration with deep network priors: a study on longitudinal PET images
Inspired by Deep Image Prior, this paper introduces a different use of deep architectures as regularizers to tackle the image registration question.
Memory-aware curriculum federated learning for breast cancer classification
Our curriculum controls the order of the training samples paying special attention to those that are forgotten after the deployment of the global model.
3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching
We discuss solutions for the exact and inexact graph isomorphism problems and recall the main spectral properties of the combinatorial graph Laplacian; We provide a novel analysis of the commute-time embedding that allows us to interpret the latter in terms of the PCA of a graph, and to select the appropriate dimension of the associated embedded metric space; We derive a unit hyper-sphere normalization for the commute-time embedding that allows us to register two shapes with different samplings; We propose a novel method to find the eigenvalue-eigenvector ordering and the eigenvector signs using the eigensignature (histogram) which is invariant to the isometric shape deformations and fits well in the spectral graph matching framework, and we present a probabilistic shape matching formulation using an expectation maximization point registration algorithm which alternates between aligning the eigenbases and finding a vertex-to-vertex assignment.
Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation
Thanks to the VAE, our model learns a shared cross-domain latent space that follows a normal distribution, which reduces the domain shift.
Articulated Shape Matching Using Laplacian Eigenfunctions and Unsupervised Point Registration
Matching articulated shapes represented by voxel-sets reduces to maximal sub-graph isomorphism when each set is described by a weighted graph.
Local-mean preserving post-processing step for non-negativity enforcement in PET imaging: application to $^{90}$Y-PET
Our idea is to transfer the negative intensities to neighboring voxels, so that the mean of the image is preserved.
Lightweight U-Net for High-Resolution Breast Imaging
We study the fully convolutional neural networks in the context of malignancy detection for breast cancer screening.
IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric Ultrasound
We present an accurate, fast and efficient method for segmentation and muscle mask propagation in 3D freehand ultrasound data, towards accurate volume quantification.
Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty
In this paper, we propose a method for the automatic classification of proximal femur fractures into 3 and 7 AO classes based on a Convolutional Neural Network (CNN).
Medical-based Deep Curriculum Learning for Improved Fracture Classification
Current deep-learning based methods do not easily integrate to clinical protocols, neither take full advantage of medical knowledge.
Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
We demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray images according to the AO classification.
Redefining Ultrasound Compounding: Computational Sonography
By providing a novel paradigm for the acquisition and reconstruction of tracked freehand 3D ultrasound, this work presents the concept of Computational Sonography (CS) to model the directionality of ultrasound information.
Weakly-Supervised Localization and Classification of Proximal Femur Fractures
In this paper, we target the problem of fracture classification from clinical X-Ray images towards an automated Computer Aided Diagnosis (CAD) system.
Capsule Networks against Medical Imaging Data Challenges
A key component to the success of deep learning is the availability of massive amounts of training data.
A Deep Metric for Multimodal Registration
Multimodal registration is a challenging problem in medical imaging due the high variability of tissue appearance under different imaging modalities.
Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies
In an unsupervised context, i. e., no prior model of the moving object(s) is available, such a structure has to be learned from the data in a bottom-up fashion.
