38 citations · 81 across the 13 of their papers we have counts for
7 papers · 1 filter
Synthesizing Post-Acetazolamide Cerebral Blood Flow Maps from Baseline MRI in Moyamoya Using 3D Generative AI
Julia Huang, Camila Gonzalez, Rydham Goyal +5
For patients with Moyamoya disease, impaired cerebrovascular reserve (CVR) is an important hemodynamic criterion for recommending extracranial-to-intracranial bypass surgery. Stand…
Unlocking Robust Segmentation Across All Age Groups via Continual Learning
Chih-Ying Liu, Jeya Maria Jose Valanarasu, Camila Gonzalez +3
Most deep learning models in medical imaging are trained on adult data with unclear performance on pediatric images. In this work, we aim to address this challenge in the context o…
Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation
Camila Gonzalez, Karol Gotkowski, Moritz Fuchs +5
Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high res…
Quality monitoring of federated Covid-19 lesion segmentation
Camila Gonzalez, Christian Harder, Amin Ranem +5
Federated Learning is the most promising way to train robust Deep Learning models for the segmentation of Covid-19-related findings in chest CTs. By learning in a decentralized fas…
How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?
Antoine Sanner, Camila Gonzalez, Anirban Mukhopadhyay
The recent achievements of Deep Learning rely on the test data being similar in distribution to the training data. In an ideal case, Deep Learning models would achieve Out-of-Distr…
Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation
Marius Memmel, Camila Gonzalez, Anirban Mukhopadhyay
Deep learning for medical imaging suffers from temporal and privacy-related restrictions on data availability. To still obtain viable models, continual learning aims to train in se…
Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation
Camila Gonzalez, Karol Gotkowski, Andreas Bucher +3
Automatic segmentation of lung lesions in computer tomography has the potential to ease the burden of clinicians during the Covid-19 pandemic. Yet predictive deep learning models a…