204 citations · 381 across the 42 of their papers we have counts for
44 papers
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…
Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation
André Ferreira, Tiago Jesus, Behrus Puladi +3
This paper presents the winning solution of task 1 and the third-placed solution of task 3 of the BraTS challenge. The use of automated tools in clinical practice has increased due…
Brain Tumour Removing and Missing Modality Generation using 3D WDM
André Ferreira, Gijs Luijten, Behrus Puladi +3
This paper presents the second-placed solution for task 8 and the participation solution for task 7 of BraTS 2024. The adoption of automated brain analysis algorithms to support cl…
Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy
Nikoo Moradi, André Ferreira, Behrus Puladi +5
Radiation therapy (RT) is essential in treating head and neck cancer (HNC), with magnetic resonance imaging(MRI)-guided RT offering superior soft tissue contrast and functional ima…
Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data
Felix J. Dorfner, Amin Dada, Felix Busch +8
Large language models (LLMs) have shown potential in biomedical applications, leading to efforts to fine-tune them on domain-specific data. However, the effectiveness of this appro…
Tumor likelihood estimation on MRI prostate data by utilizing k-Space information
M. Rempe, F. Hörst, C. Seibold +7
We present a novel preprocessing and prediction pipeline for the classification of magnetic resonance imaging (MRI) that takes advantage of the information rich complex valued k-Sp…
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
Ahmad Idrissi-Yaghir, Amin Dada, Henning Schäfer +17
Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate…
Deep Learning-based Point Cloud Registration for Augmented Reality-guided Surgery
Maximilian Weber, Daniel Wild, Jens Kleesiek +2
Point cloud registration aligns 3D point clouds using spatial transformations. It is an important task in computer vision, with applications in areas such as augmented reality (AR)…
Does Biomedical Training Lead to Better Medical Performance?
Amin Dada, Marie Bauer, Amanda Butler Contreras +4
Large Language Models (LLMs) are expected to significantly contribute to patient care, diagnostics, and administrative processes. Emerging biomedical LLMs aim to address healthcare…
A Semi-automatic Cranial Implant Design Tool Based on Rigid ICP Template Alignment and Voxel Space Reconstruction
Michael Lackner, Behrus Puladi, Jens Kleesiek +2
In traumatic medical emergencies, the patients heavily depend on cranioplasty - the craft of neurocranial repair using cranial implants. Despite the improvements made in recent yea…
Deep PCCT: Photon Counting Computed Tomography Deep Learning Applications Review
Ana Carolina Alves, André Ferreira, Gijs Luijten +4
Medical imaging faces challenges such as limited spatial resolution, interference from electronic noise and poor contrast-to-noise ratios. Photon Counting Computed Tomography (PCCT…
Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images
Johannes Raufeisen, Kunpeng Xie, Fabian Hörst +9
Background: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine…
How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation
André Ferreira, Naida Solak, Jianning Li +4
Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the me…
Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy
Zdravko Marinov, Paul F. Jäger, Jan Egger +2
Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback ta…
Multisensory extended reality applications offer benefits for volumetric biomedical image analysis in research and medicine
Kathrin Krieger, Jan Egger, Jens Kleesiek +2
3D data from high-resolution volumetric imaging is a central resource for diagnosis and treatment in modern medicine. While the fast development of AI enhances imaging and analysis…
On the Impact of Cross-Domain Data on German Language Models
Amin Dada, Aokun Chen, Cheng Peng +12
Traditionally, large language models have been either trained on general web crawls or domain-specific data. However, recent successes of generative large language models, have she…