3 citations · 6 across the 4 of their papers we have counts for
4 papers
SALT: Introducing a Framework for Hierarchical Segmentations in Medical Imaging using Softmax for Arbitrary Label Trees
Sven Koitka, Giulia Baldini, Cynthia S. Schmidt +11
Traditional segmentation networks approach anatomical structures as standalone elements, overlooking the intrinsic hierarchical connections among them. This study introduces Softma…
Gadolinium dose reduction for brain MRI using conditional deep learning
Thomas Pinetz, Erich Kobler, Robert Haase +7
Recently, deep learning (DL)-based methods have been proposed for the computational reduction of gadolinium-based contrast agents (GBCAs) to mitigate adverse side effects while pre…
Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines
Alexander Jaus, Constantin Seibold, Kelsey Hermann +5
In this study, we present a method for generating automated anatomy segmentation datasets using a sequential process that involves nnU-Net-based pseudo-labeling and anatomy-guided…
Logion: Machine Learning for Greek Philology
Charlie Cowen-Breen, Creston Brooks, Johannes Haubold +1
This paper presents machine-learning methods to address various problems in Greek philology. After training a BERT model on the largest premodern Greek dataset used for this purpos…