126 citations · 522 across the 19 of their papers we have counts for
19 papers
Can ultrasound confidence maps predict sonographers' labeling variability?
Vanessa Gonzalez Duque, Leonhard Zirus, Yordanka Velikova +2
Measuring cross-sectional areas in ultrasound images is a standard tool to evaluate disease progress or treatment response. Often addressed today with supervised deep-learning segm…
Ultrasound Image Reconstruction with Denoising Diffusion Restoration Models
Yuxin Zhang, Clément Huneau, Jérôme Idier +1
Ultrasound image reconstruction can be approximately cast as a linear inverse problem that has traditionally been solved with penalized optimization using the or norm,…
Deformable image registration with deep network priors: a study on longitudinal PET images
Constance Fourcade, Ludovic Ferrer, Noemie Moreau +10
Longitudinal image registration is challenging and has not yet benefited from major performance improvements thanks to deep-learning. Inspired by Deep Image Prior, this paper intro…
Memory-aware curriculum federated learning for breast cancer classification
Amelia Jiménez-Sánchez, Mickael Tardy, Miguel A. González Ballester +2
For early breast cancer detection, regular screening with mammography imaging is recommended. Routinary examinations result in datasets with a predominant amount of negative sample…
Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation
Dawood Al Chanti, Diana Mateus
This paper addresses the domain shift problem for segmentation. As a solution, we propose OLVA, a novel and lightweight unsupervised domain adaptation method based on a Variational…
Articulated Shape Matching Using Laplacian Eigenfunctions and Unsupervised Point Registration
Diana Mateus, Radu Horaud, David Knossow +2
Matching articulated shapes represented by voxel-sets reduces to maximal sub-graph isomorphism when each set is described by a weighted graph. Spectral graph theory can be used to…
Local-mean preserving post-processing step for non-negativity enforcement in PET imaging: application to Y-PET
Maël Millardet, Saïd Moussaoui, Diana Mateus +2
In a low-statistics PET imaging context, the positive bias in regions of low activity is a burning issue. To overcome this problem, algorithms without the built-in non-negativity c…
Lightweight U-Net for High-Resolution Breast Imaging
Mickael Tardy, Diana Mateus
We study the fully convolutional neural networks in the context of malignancy detection for breast cancer screening. We work on a supervised segmentation task looking for an accept…
IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric Ultrasound
Dawood Al Chanti, Vanessa Gonzalez Duque, Marion Crouzier +3
We present an accurate, fast and efficient method for segmentation and muscle mask propagation in 3D freehand ultrasound data, towards accurate volume quantification. A deep Siames…
Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty
Amelia Jiménez-Sánchez, Diana Mateus, Sonja Kirchhoff +5
An adequate classification of proximal femur fractures from X-ray images is crucial for the treatment choice and the patients' clinical outcome. We rely on the commonly used AO sys…
Improving Mammography Malignancy Segmentation by Designing the Training Process
Mickael Tardy, Diana Mateus
We work on the breast imaging malignancy segmentation task while focusing on the training process instead of network complexity. We designed a training process based on a modified…
Medical-based Deep Curriculum Learning for Improved Fracture Classification
Amelia Jiménez-Sánchez, Diana Mateus, Sonja Kirchhoff +5
Current deep-learning based methods do not easily integrate to clinical protocols, neither take full advantage of medical knowledge. In this work, we propose and compare several st…
A closer look onto breast density with weakly supervised dense-tissue masks
Mickael Tardy, Bruno Scheffer, Diana Mateus
This work focuses on the automatic quantification of the breast density from digital mammography imaging. Using only categorical image-wise labels we train a model capable of predi…
Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni +5
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 ima…
Redefining Ultrasound Compounding: Computational Sonography
Rüdiger Göbl, Diana Mateus, Christoph Hennersperger +2
Freehand three-dimensional ultrasound (3D-US) has gained considerable interest in research, but even today suffers from its high inter-operator variability in clinical practice. Th…
Weakly-Supervised Localization and Classification of Proximal Femur Fractures
Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni +5
In this paper, we target the problem of fracture classification from clinical X-Ray images towards an automated Computer Aided Diagnosis (CAD) system. Although primarily dealing wi…
Capsule Networks against Medical Imaging Data Challenges
Amelia Jiménez-Sánchez, Shadi Albarqouni, Diana Mateus
A key component to the success of deep learning is the availability of massive amounts of training data. Building and annotating large datasets for solving medical image classifica…
A Deep Metric for Multimodal Registration
Martin Simonovsky, Benjamín Gutiérrez-Becker, Diana Mateus +2
Multimodal registration is a challenging problem in medical imaging due the high variability of tissue appearance under different imaging modalities. The crucial component here is…
Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies
Fabio Cuzzolin, Diana Mateus, Radu Horaud
In motion analysis and understanding it is important to be able to fit a suitable model or structure to the temporal series of observed data, in order to describe motion patterns i…