3 citations · 4 across the 6 of their papers we have counts for
6 papers
Anatomy-Aware 3D Mesh Refinement of Pericardium Segmentations on Computed Tomography
Andreas W. Aspe, Jonas Jalili Loft, Michael Huy Cuong Pham +6
Accurate delineation of the pericardium in a cardiac CT scan is essential for quantifying epicardial adipose tissue, yet it remains one of the most challenging structures to segmen…
Automated Cardiac Adipose Tissue Segmentation in Computed Tomography: A Literature Review
Andreas W. Aspe, Jonas Jalili Pedersen, Andreas Ohrt Johansen +4
This review provides an overview of recent advancements in automated segmentation methods on Computed Tomography (CT) for two types of cardiac fat: Epicardial adipose Tissue (EAT)…
Looking into Concept Explanation Methods for Diabetic Retinopathy Classification
Andrea M. Storås, Josefine V. Sundgaard
Diabetic retinopathy is a common complication of diabetes, and monitoring the progression of retinal abnormalities using fundus imaging is crucial. Because the images must be inter…
Was that so hard? Estimating human classification difficulty
Morten Rieger Hannemose, Josefine Vilsbøll Sundgaard, Niels Kvorning Ternov +2
When doctors are trained to diagnose a specific disease, they learn faster when presented with cases in order of increasing difficulty. This creates the need for automatically esti…
EyeLoveGAN: Exploiting domain-shifts to boost network learning with cycleGANs
Josefine Vilsbøll Sundgaard, Kristine Aavild Juhl, Jakob Mølkjær Slipsager
This paper presents our contribution to the REFUGE challenge 2020. The challenge consisted of three tasks based on a dataset of retinal images: Segmentation of optic disc and cup,…
Multi-modal data generation with a deep metric variational autoencoder
Josefine Vilsbøll Sundgaard, Morten Rieger Hannemose, Søren Laugesen +6
We present a deep metric variational autoencoder for multi-modal data generation. The variational autoencoder employs triplet loss in the latent space, which allows for conditional…