14 citations · 22 across the 8 of their papers we have counts for
8 papers
HemExp: Clinically-Guided Latent Diffusion for Modeling Hematoma Expansion
Orhun Utku Aydin, Satoru Tanioka, Tzu I Chuang +7
Hematoma expansion (HE) after spontaneous intracerebral hemorrhage (ICH) is a major determinant of acute triage and treatment decisions in neurosurgical care. However, most existin…
RELICT: A Replica Detection Framework for Medical Image Generation
Orhun Utku Aydin, Alexander Koch, Adam Hilbert +5
Despite the potential of synthetic medical data for augmenting and improving the generalizability of deep learning models, memorization in generative models can lead to unintended…
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review
Luis M. Lopez-Ramos, Florian Leiser, Aditya Rastogi +6
The joint implementation of federated learning (FL) and explainable artificial intelligence (XAI) could allow training models from distributed data and explaining their inner worki…
Cross-modality image synthesis from TOF-MRA to CTA using diffusion-based models
Alexander Koch, Orhun Utku Aydin, Adam Hilbert +4
Cerebrovascular disease often requires multiple imaging modalities for accurate diagnosis, treatment, and monitoring. Computed Tomography Angiography (CTA) and Time-of-Flight Magne…
The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography
Kaiyuan Yang, Fabio Musio, Yihui Ma +112
The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to influence the risk, severity, an…
From Single-Hospital to Multi-Centre Applications: Enhancing the Generalisability of Deep Learning Models for Adverse Event Prediction in the ICU
Patrick Rockenschaub, Adam Hilbert, Tabea Kossen +3
Deep learning (DL) can aid doctors in detecting worsening patient states early, affording them time to react and prevent bad outcomes. While DL-based early warning models usually w…
On The Usage Of Average Hausdorff Distance For Segmentation Performance Assessment: Hidden Bias When Used For Ranking
Orhun Utku Aydin, Abdel Aziz Taha, Adam Hilbert +5
Average Hausdorff Distance (AVD) is a widely used performance measure to calculate the distance between two point sets. In medical image segmentation, AVD is used to compare ground…
Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networks
Tabea Kossen, Pooja Subramaniam, Vince I. Madai +9
Anonymization and data sharing are crucial for privacy protection and acquisition of large datasets for medical image analysis. This is a big challenge, especially for neuroimaging…