3 citations · 6 across the 6 of their papers we have counts for
8 papers
Made to measure: an introduction to quantification in microscopy data
Siân Culley, Alicia Cuber Caballero, Jemima J Burden +1
Images are at the core of most modern biological experiments and are used as a major source of quantitative information. Numerous algorithms are available to process images and mak…
Deep learning for bioimage analysis
Adrien Hallou, Hannah Yevick, Bianca Dumitrascu +1
Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage dat…
aura-net : robust segmentation of phase-contrast microscopy images with few annotations
Ethan Cohen, Virginie Uhlmann
We present AURA-net, a convolutional neural network (CNN) for the segmentation of phase-contrast microscopy images. AURA-net uses transfer learning to accelerate training and Atten…
Dictionary Learning for Two-Dimensional Kendall Shapes
Anna Song, Virginie Uhlmann, Julien Fageot +1
We propose a novel sparse dictionary learning method for planar shapes in the sense of Kendall, namely configurations of landmarks in the plane considered up to similitudes. Our sh…
Support and Approximation Properties of Hermite Splines
Julien Fageot, Shayan Aziznejad, Michael Unser +1
In this paper, we formally investigate two mathematical aspects of Hermite splines which translate to features that are relevant to their practical applications. We first demonstra…
Principled Design and Implementation of Steerable Detectors
Julien Fageot, Virginie Uhlmann, Zsuzsanna Püspöki +3
We provide a complete pipeline for the detection of patterns of interest in an image. In our approach, the patterns are assumed to be adequately modeled by a known template, and ar…