141 citations · 332 across the 67 of their papers we have counts for
72 papers
Optimization Encoders: Rethinking Second-Order Meta-Learning for Neural Fields
Rudolf L. M. van Herten, Soufiane Ben Haddou, Rachit Saluja +1
Conditional neural fields represent signals continuously, but their effectiveness depends on how the conditional latent representations are inferred from observed data. In meta-lea…
Sparse cubical complexes for efficient topology-preservation in image data
Alexander H. Berger, Marco Fontana, Daniel Rueckert +3
Persistent homology (PH) is a frequently used tool for extracting and preserving topological information from image data, particularly in image segmentation, where preservation of…
It depends: Incorporating correlations for joint aleatoric and epistemic uncertainties of high-dimensional output spaces
Leonhard F. Feiner, Manuel Nickel, Martin Menten +6
Uncertainty Quantification (UQ) plays a vital role in enhancing the reliability of deep learning model predictions, especially in scenarios with high-dimensional output spaces. Thi…
GeoPose: Patient-agnostic CTA-to-DSA registration through projection-space calibration
Rudolf L. M. van Herten, Robert Graf, Paula Feldman +1
Aligning intraoperative biplanar digital subtraction angiography (DSA) to pre-procedural computed tomography angiography (CTA) requires rapid and accurate 3D-to-2D registration. Op…
Unifying Active Learning and Semi-Supervised Learning for Medical Image Segmentation
Bahram Jafrasteh, Cheng Wan, Heejong Kim +2
In practical settings, medical image segmentation models are often developed with limited annotated data rather than fully labeled datasets. Training frequently begins in ultra-low…
Beyond scalar losses: calibrating segmentation models via gradient vector field surgery
Laurin Lux, Alexander H. Berger, Moritz Knolle +2
Region-based loss functions, such as the Dice loss, have established themselves as the de facto standard for highly class- and region-imbalanced segmentation tasks. However, models…
MAdam: Metric-Aware Multi-Objective Adam
Fengbei Liu, Rachit Saluja, Sunwoo Kwak +5
Multi-objective optimization (MOO) underlies many machine learning problems, yet MOO solvers across the loss-balancing, gradient-balancing, and Pareto-based families almost univers…
A Multimodal 3D Foundation Model for Light Sheet Fluorescence Microscopy Enables Few-Shot Segmentation, Classification, and Deblurring
Adina Scheinfeld, Haotan Zhang, Shang Mu +5
Light sheet fluorescence microscopy (LSM) enables high-resolution, three-dimensional (3D) imaging of biological specimens, providing rich volumetric data for studying cellular orga…
MELD: Multi-Task Equilibrated Learning Detector for AI-Generated Text
Chenjun Li, Cheng Wan, Johannes C. Paetzold +1
Large language models are widely used in everyday writing, making reliable AI-generated text detection crucial for academic integrity, content moderation, and provenance tracking.…
VERITAS: A Multi-Agent Co-Scientist for Verifiable Image-Derived Hypothesis Testing
Lucas Stoffl, Benedikt Wiestler, Johannes C. Paetzold
Scientific research based on multimodal clinical data (including medical imaging) requires coordinating clinical, radiological, programming, and biostatistical expertise, a fragmen…
Synthetic Vasculature and Pathology Enhance Vision-Language Model Reasoning
Chenjun Li, Cheng Wan, Laurin Lux +4
Vision-Language Models (VLMs) offer a promising path toward interpretable medical diagnosis by allowing users to ask about clinical explanations alongside predictions and across di…
The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning
Akis Linardos, Sarthak Pati, Ujjwal Baid +25
We present the design and results of the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024, which focuses on federated learning (FL) for glioma sub-region segmentation in m…
BackSplit: The Importance of Sub-dividing the Background in Biomedical Lesion Segmentation
Rachit Saluja, Asli Cihangir, Ruining Deng +3
Segmenting small lesions in medical images remains notoriously difficult. Most prior work tackles this challenge by either designing better architectures, loss functions, or data a…
Graph Conditioned Diffusion for Controllable Histopathology Image Generation
Sarah Cechnicka, Matthew Baugh, Weitong Zhang +5
Recent advances in Diffusion Probabilistic Models (DPMs) have set new standards in high-quality image synthesis. Yet, controlled generation remains challenging, particularly in sen…
Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine
Robert Graf, Tanja Lerchl, Kati Nispel +7
Digital twins offer a powerful framework for subject-specific simulation and clinical decision support, yet their development often hinges on accurate, individualized anatomical mo…
Topology Optimization in Medical Image Segmentation with Fast Euler Characteristic
Liu Li, Qiang Ma, Cheng Ouyang +3
Deep learning-based medical image segmentation techniques have shown promising results when evaluated based on conventional metrics such as the Dice score or Intersection-over-Unio…
Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort
Hendrik Möller, Hanna Schön, Alina Dima +10
Thoracolumbar stump ribs are one of the essential indicators of thoracolumbar transitional vertebrae or enumeration anomalies. While some studies manually assess these anomalies an…
Skelite: Compact Neural Networks for Efficient Iterative Skeletonization
Luis D. Reyes Vargas, Martin J. Menten, Johannes C. Paetzold +2
Skeletonization extracts thin representations from images that compactly encode their geometry and topology. These representations have become an important topological prior for pr…
A Graph-Based Framework for Interpretable Whole Slide Image Analysis
Alexander Weers, Alexander H. Berger, Laurin Lux +3
The histopathological analysis of whole-slide images (WSIs) is fundamental to cancer diagnosis but is a time-consuming and expert-driven process. While deep learning methods show p…
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis
Chenjun Li, Laurin Lux, Alexander H. Berger +3
Accurate staging of Diabetic Retinopathy (DR) is essential for guiding timely interventions and preventing vision loss. However, current staging models are hardly interpretable, an…
MAGO-SP: Detection and Correction of Water-Fat Swaps in Magnitude-Only VIBE MRI
Robert Graf, Hendrik Möller, Sophie Starck +15
Volume Interpolated Breath-Hold Examination (VIBE) MRI generates images suitable for water and fat signal composition estimation. While the two-point VIBE provides water-fat-separa…
Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations
Laurin Lux, Alexander H. Berger, Maria Romeo Tricas +8
Interpretability is crucial for utilizing machine learning models as clinical decision support tools for medical diagnostics. However, most state-of-the-art image classifiers based…
SELMA3D challenge: Self-supervised learning for 3D light-sheet microscopy image segmentation
Ying Chen, Rami Al-Maskari, Izabela Horvath +14
Recent innovations in light sheet microscopy, paired with developments in tissue clearing techniques, enable the 3D imaging of large mammalian tissues with cellular resolution. Com…
Pitfalls of topology-aware image segmentation
Alexander H. Berger, Laurin Lux, Alexander Weers +3
Topological correctness, i.e., the preservation of structural integrity and specific characteristics of shape, is a fundamental requirement for medical imaging tasks, such as neuro…