1.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Ludwig-Maximilians-Universität MünchenDE63 papers
- Technical University of MunichDE41 papers
- LMU KlinikumDE29 papers
- Imperial College LondonGB8 papers
- Helmholtz MunichDE6 papers
- TUM KlinikumDE6 papers
- Zimmer Biomet (Germany)DE6 papers
- Fraunhofer Institute for Integrated CircuitsDE4 papers
- Munich School of PhilosophyDE4 papers
- TU Dortmund UniversityDE4 papers
- University College LondonGB4 papers
- Zimmer Biomet (Netherlands)NL4 papers
12 papers · 1 filter
3D Segmentation Using Viewpoint-Dependent Spatial Relationships
Ayaka Nanri, Klara Reichard, Mert Kiray +3
Recent advances in 3D datasets and multimodal models have greatly improved natural language 3D scene understanding. However, most 3D referring segmentation methods do not explicitl…
ConVibNet: Needle Detection during Continuous Insertion via Frequency-Inspired Features
Jiamei Guo, Zhehao Duan, Maria Neiiendam +3
Purpose: Ultrasound-guided needle interventions are widely used in clinical practice, but their success critically depends on accurate needle placement, which is frequently hindere…
US-X Complete: A Multi-Modal Approach to Anatomical 3D Shape Recovery
Miruna-Alexandra Gafencu, Yordanka Velikova, Nassir Navab +1
Ultrasound offers a radiation-free, cost-effective solution for real-time visualization of spinal landmarks, paraspinal soft tissues and neurovascular structures, making it valuabl…
Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings
Christian Grashei, Christian Brechenmacher, Rao Muhammad Umer +4
Pathology foundation models (FMs) have driven significant progress in computational pathology. However, these high-performing models can easily exceed a billion parameters and prod…
MultiMAE for Brain MRIs: Robustness to Missing Inputs Using Multi-Modal Masked Autoencoder
Ayhan Can Erdur, Christian Beischl, Daniel Scholz +4
Missing input sequences are common in medical imaging data, posing a challenge for deep learning models reliant on complete input data. In this work, inspired by MultiMAE [2], we d…
DeepAf: One-Shot Spatiospectral Auto-Focus Model for Digital Pathology
Yousef Yeganeh, Maximilian Frantzen, Michael Lee +3
While Whole Slide Imaging (WSI) scanners remain the gold standard for digitizing pathology samples, their high cost limits accessibility in many healthcare settings. Other low-cost…
Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration
Bailiang Jian, Jiazhen Pan, Morteza Ghahremani +3
Our findings indicate that adopting "advanced" computational elements fails to significantly improve registration accuracy. Instead, well-established registration-specific designs…
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…
PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds
Zhaiyu Chen, Yilei Shi, Liangliang Nan +2
We present PolyGNN, a polyhedron-based graph neural network for 3D building reconstruction from point clouds. PolyGNN learns to assemble primitives obtained by polyhedral decomposi…
Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments
Felix Ott, Lucas Heublein, David Rügamer +2
The localization of objects is a crucial task in various applications such as robotics, virtual and augmented reality, and the transportation of goods in warehouses. Recent advance…
Automated wildlife image classification: An active learning tool for ecological applications
Ludwig Bothmann, Lisa Wimmer, Omid Charrakh +6
Wildlife camera trap images are being used extensively to investigate animal abundance, habitat associations, and behavior, which is complicated by the fact that experts must first…
Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark
Fang Xu, Yilei Shi, Patrick Ebel +2
Cloud removal is a significant and challenging problem in remote sensing, and in recent years, there have been notable advancements in this area. However, two major issues remain h…