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
36 papers · 1 filter
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…
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…
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…
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…
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…
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…
Cluster-based human-in-the-loop strategy for improving machine learning-based circulating tumor cell detection in liquid biopsy
Hümeyra Husseini-Wüsthoff, Sabine Riethdorf, Andreas Schneeweiss +5
Detection and differentiation of circulating tumor cells (CTCs) and non-CTCs in blood draws of cancer patients pose multiple challenges. While the gold standard relies on tedious m…
Comparison of marker-less 2D image-based methods for infant pose estimation
Lennart Jahn, Sarah Flügge, Dajie Zhang +5
In this study we compare the performance of available generic- and infant-pose estimators for a video-based automated general movement assessment (GMA), and the choice of viewing a…
SynBench: A Synthetic Benchmark for Non-rigid 3D Point Cloud Registration
Sara Monji-Azad, Marvin Kinz, Claudia Scherl +3
Non-rigid point cloud registration is a crucial task in computer vision. Evaluating a non-rigid point cloud registration method requires a dataset with challenges such as large def…
Deep learning based detection of collateral circulation in coronary angiographies
Cosmin-Andrei Hatfaludi, Daniel Bunescu, Costin Florian Ciusdel +9
Coronary artery disease (CAD) is the dominant cause of death and hospitalization across the globe. Atherosclerosis, an inflammatory condition that gradually narrows arteries and ha…
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…
Benchmarking Pathology Feature Extractors for Whole Slide Image Classification
Georg Wölflein, Dyke Ferber, Asier R. Meneghetti +6
Weakly supervised whole slide image classification is a key task in computational pathology, which involves predicting a slide-level label from a set of image patches constituting…
Application-driven Validation of Posteriors in Inverse Problems
Tim J. Adler, Jan-Hinrich Nölke, Annika Reinke +8
Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist. In response, poste…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Clinical application of HEDI for biomechanical evaluation and visualisation in incisional hernia repair
Philipp D. Lösel, Jacob J. Relle, Samuel Voß +7
Background: Abdominal wall defects, such as incisional hernias, are a common source of pain and discomfort and often require repeated surgical interventions. Traditional mesh repai…
Taming Detection Transformers for Medical Object Detection
Marc K. Ickler, Michael Baumgartner, Saikat Roy +2
The accurate detection of suspicious regions in medical images is an error-prone and time-consuming process required by many routinely performed diagnostic procedures. To support c…
Robust-DefReg: A Robust Deformable Point Cloud Registration Method based on Graph Convolutional Neural Networks
Sara Monji-Azad, Marvin Kinz, Jürgen Hesser
Point cloud registration is a fundamental problem in computer vision that aims to estimate the transformation between corresponding sets of points. Non-rigid registration, in parti…
Effect of latent space distribution on the segmentation of images with multiple annotations
Ishaan Bhat, Josien P. W. Pluim, Max A. Viergever +1
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution t…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes
Benjamin Alt, Christian Kunz, Darko Katic +5
The semantic segmentation of surgical scenes is a prerequisite for task automation in robot assisted interventions. We propose LapSeg3D, a novel DNN-based approach for the voxel-wi…
mvHOTA: A multi-view higher order tracking accuracy metric to measure spatial and temporal associations in multi-point detection
Lalith Sharan, Halvar Kelm, Gabriele Romano +3
Multi-point tracking is a challenging task that involves detecting points in the scene and tracking them across a sequence of frames. Computing detection-based measures like the F-…