126 citations · 522 across the 19 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
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…