204 citations · 381 across the 42 of their papers we have counts for
9 papers · 1 filter
Flatness is Necessary, Neural Collapse is Not: Rethinking Generalization via Grokking
Ting Han, Linara Adilova, Henning Petzka +2
Neural collapse, i.e., the emergence of highly symmetric, class-wise clustered representations, is frequently observed in deep networks and is often assumed to reflect or enable ge…
Whom to Trust? Adaptive Collaboration in Personalized Federated Learning
Amr Abourayya, Jens Kleesiek, Bharat Rao +1
Data heterogeneity poses a fundamental challenge in federated learning (FL), especially when clients differ not only in distribution but also in the reliability of their prediction…
Deep Learning-Based Semantic Segmentation for Real-Time Kidney Imaging and Measurements with Augmented Reality-Assisted Ultrasound
Gijs Luijten, Roberto Maria Scardigno, Lisle Faray de Paiva +5
Ultrasound (US) is widely accessible and radiation-free but has a steep learning curve due to its dynamic nature and non-standard imaging planes. Additionally, the constant need to…
Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation
André Ferreira, Kunpeng Xie, Caroline Wilpert +12
AI requires extensive datasets, while medical data is subject to high data protection. Anonymization is essential, but poses a challenge for some regions, such as the head, as iden…
From Screen to Space: Evaluating Siemens' Cinematic Reality
Gijs Luijten, Lisle Faray de Paiva, Sebastian Krueger +8
As one of the first research teams with full access to Siemens' Cinematic Reality, we evaluate its usability and clinical potential for cinematic volume rendering on the Apple Visi…
Beyond the Desktop: XR-Driven Segmentation with Meta Quest 3 and MX Ink
Lisle Faray de Paiva, Gijs Luijten, Ana Sofia Ferreira Santos +4
Medical imaging segmentation is essential in clinical settings for diagnosing diseases, planning surgeries, and other procedures. However, manual annotation is a cumbersome and eff…
Towards Conditioning Clinical Text Generation for User Control
Osman Alperen Koraş, Rabi Bahnan, Jens Kleesiek +1
Deploying natural language generation systems in clinical settings remains challenging despite advances in Large Language Models (LLMs), which continue to exhibit hallucinations an…
MeDiSumQA: Patient-Oriented Question-Answer Generation from Discharge Letters
Amin Dada, Osman Alperen Koras, Marie Bauer +4
While increasing patients' access to medical documents improves medical care, this benefit is limited by varying health literacy levels and complex medical terminology. Large langu…
CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models
Fabian Hörst, Moritz Rempe, Helmut Becker +3
Digital Pathology is a cornerstone in the diagnosis and treatment of diseases. A key task in this field is the identification and segmentation of cells in hematoxylin and eosin-sta…