9.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Heidelberg UniversityDE146 papers
- German Cancer Research CenterDE68 papers
- National Center for Tumor DiseasesDE37 papers
- University Medical Center UtrechtNL18 papers
- Technical University of MunichDE15 papers
- Else Kröner Fresenius Center for Digital HealthDE14 papers
- Harvard UniversityUS14 papers
- University Hospital Carl Gustav CarusDE14 papers
- University Medical Centre MannheimDE14 papers
- RWTH Aachen UniversityDE13 papers
- Utrecht UniversityNL13 papers
- Karlsruhe Institute of TechnologyDE12 papers
156 papers
Transformation Behavior of Images in Latent Space
Christian Zöllner, Mozzam Motiwala, Aysel Ahadova +4
Training of neural networks for histopathology classification tasks typically relies on data encoding into latent space, which reduces complexity and improves performance. There ar…
Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: applications to a large-scale, clinically heterogeneous dataset
Jelmer van Lune, Stefano Mandija, Oscar van der Heide +5
Magnetic resonance imaging (MRI) is a cornerstone of clinical neuroimaging, yet conventional MRIs provide qualitative information heavily dependent on scanner hardware and acquisit…
Unsupervised Segmentation of Micro-CT Scans of Polyurethane Structures By Combining Hidden-Markov-Random Fields and a U-Net
Julian Grolig, Lars Griem, Michael Selzer +4
Extracting digital material representations from images is a necessary prerequisite for a quantitative analysis of material properties. Different segmentation approaches have been…
Gut decisions based on the liver: A radiomics approach to boost colorectal cancer screening
Anna Hinterberger, Jonas Bohn, Dasha Trofimova +13
Non-invasive colorectal cancer (CRC) screening represents a key opportunity to improve colonoscopy participation rates and reduce CRC mortality. This study explores the potential o…
Deformable Image Registration for Self-supervised Cardiac Phase Detection in Multi-View Multi-Disease Cardiac Magnetic Resonance Images
Sven Koehler, Sarah Kaye Mueller, Jonathan Kiekenap +6
Cardiovascular magnetic resonance (CMR) is the gold standard for assessing cardiac function, but individual cardiac cycles complicate automatic temporal comparison or sub-phase ana…
Hallucination Filtering in Radiology Vision-Language Models Using Discrete Semantic Entropy
Patrick Wienholt, Sophie Caselitz, Robert Siepmann +6
To determine whether using discrete semantic entropy (DSE) to reject questions likely to generate hallucinations can improve the accuracy of black-box vision-language models (VLMs)…
Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
Soroosh Tayebi Arasteh, Mina Shaigan, Christiane Kuhl +3
Self-supervised learning (SSL) has improved visual representation learning, but its value in chest radiography remains uncertain. DINOv3 extends earlier SSL models through Gram-anc…
Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
Max Kirchner, Hanna Hoffmann, Alexander C. Jenke +16
Developing generalizable surgical AI requires multi-institutional data, yet privacy constraints preclude direct data sharing, making Federated Learning (FL) a natural candidate. It…
Millennium Pathways for Tractography: 40 grand challenges to shape the future of tractography
Maxime Descoteaux, Kurt G. Schilling, Dogu Baran Aydogan +44
In the spirit of the historic Millennium Prize Problems that heralded a new era for mathematics, the newly formed International Society for Tractography (IST) has launched the Mill…
The Missing Piece: A Case for Pre-Training in 3D Medical Object Detection
Katharina Eckstein, Constantin Ulrich, Michael Baumgartner +5
Large-scale pre-training holds the promise to advance 3D medical object detection, a crucial component of accurate computer-aided diagnosis. Yet, it remains underexplored compared…
MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification
Patrick Wienholt, Christiane Kuhl, Jakob Nikolas Kather +2
Deep neural networks excel in radiological image classification but frequently suffer from poor interpretability, limiting clinical acceptance. We present MedicalPatchNet, an inher…
Characterization of a novel plastic scintillation detector for in vivo electron dosimetry
Cornelius J. Bauer, Frank Schneider, Ida D. Göbel +3
Introduction: Real-time dosimetry of surface doses in electron beams has not been widely established yet. Plastic scintillation detectors (PSD) promise high spatial resolution and…
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence
Maximilian Ferle, Jonas Ader, Thomas Wiemers +7
Risk stratification is a key tool in clinical decision-making, yet current approaches often fail to translate sophisticated survival analysis into actionable clinical criteria. We…
A Tutorial on MRI Reconstruction: From Modern Methods to Clinical Implications
Tolga Çukur, Salman U. H. Dar, Valiyeh Ansarian Nezhad +4
MRI is an indispensable clinical tool, offering a rich variety of tissue contrasts to support broad diagnostic and research applications. Clinical exams routinely acquire multiple…
Implicit neural representations for accurate estimation of the standard model of white matter
Tom Hendriks, Gerrit Arends, Edwin Versteeg +3
Diffusion magnetic resonance imaging (dMRI) enables non-invasive investigation of tissue microstructure. The Standard Model (SM) of white matter aims to disentangle dMRI signal con…
Direct optimization of the probability of lesion origin in proton treatment planning for low-grade glioma patients
Tim Ortkamp, Habiba Sallem, Semi Harrabi +4
In proton therapy of low-grade glioma (LGG) patients, contrast-enhancing brain lesions (CEBLs) on magnetic resonance imaging are considered predictive of late radiation-induced les…
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp +67
Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI an…
Helium Range Viability for Online Range Probing in Mixed Carbon-Helium Beams
Jennifer J. Hardt, Alexander A. Pryanichnikov, Oliver Jäkel +2
Background: Recently, mixed carbon-helium beams were proposed for range verification in carbon ion therapy: Helium, with three times the range of carbon, serves as an on-line range…
Design for a Digital Twin in Clinical Patient Care
Anna-Katharina Nitschke, Carlos Brandl, Fabian Egersdörfer +3
Digital Twins hold great potential to personalize clinical patient care, provided the concept is translated to meet specific requirements emerging from established clinical workflo…
GeoDEN: A Visual Exploration Tool for Analysing the Geographic Spread of Dengue Serotypes
Aidan Marler, Yannik Roell, Steffen Knoblauch +3
Static maps and animations remain popular in spatial epidemiology of dengue, limiting the analytical depth and scope of visualisations. Over half of the global population live in d…
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning
Maximilian Fischer, Peter Neher, Peter Schüffler +9
Accurate diagnosis of disease often depends on the exhaustive examination of Whole Slide Images (WSI) at microscopic resolution. Efficient handling of these data-intensive images r…
KMT2B-related disorders: expansion of the phenotypic spectrum and long-term efficacy of deep brain stimulation
L Cif, D Demailly, JP Lin +112
Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor feat…
Novel computational workflows for natural and biomedical image processing based on hypercomplex algebras
Nektarios A. Valous, Eckhard Hitzer, Dragoş Duşe +12
Hypercomplex image processing extends conventional techniques in a unified paradigm encompassing algebraic and geometric principles. This work leverages quaternions and the two-dim…
MyDigiTwin: A Privacy-Preserving Framework for Personalized Cardiovascular Risk Prediction and Scenario Exploration
Héctor Cadavid, Hyunho Mo, Bauke Arends +5
Cardiovascular disease (CVD) remains a leading cause of death, and primary prevention through personalized interventions is crucial. This paper introduces MyDigiTwin, a framework t…