41 citations · 137 across the 34 of their papers we have counts for
12 papers · 1 filter
MedNeXt-v2: Scaling 3D ConvNeXts for Large-Scale Supervised Representation Learning in Medical Image Segmentation
Saikat Roy, Yannick Kirchhoff, Constantin Ulrich +4
Large-scale supervised pretraining is rapidly reshaping 3D medical image segmentation. However, existing efforts focus primarily on increasing dataset size and overlook the questio…
CRONOS: Continuous Time Reconstruction for 4D Medical Longitudinal Series
Nico Albert Disch, Saikat Roy, Constantin Ulrich +5
Forecasting how 3D medical scans evolve over time is important for disease progression, treatment planning, and developmental assessment. Yet existing models either rely on a singl…
VoxTell: Free-Text Promptable Universal 3D Medical Image Segmentation
Maximilian Rokuss, Moritz Langenberg, Yannick Kirchhoff +8
We introduce VoxTell, a vision-language model for text-prompted volumetric medical image segmentation. It maps free-form descriptions, from single words to full clinical sentences,…
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…
Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imaging
Nico Albert Disch, Yannick Kirchhoff, Robin Peretzke +5
Understanding temporal dynamics in medical imaging is crucial for applications such as disease progression modeling, treatment planning and anatomical development tracking. However…
Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation
Constantin Ulrich, Tassilo Wald, Fabian Isensee +1
The segmentation of lesions in Moderate to Severe Traumatic Brain Injury (msTBI) presents a significant challenge in neuroimaging due to the diverse characteristics of these lesion…
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…
nnInteractive: Redefining 3D Promptable Segmentation
Fabian Isensee, Maximilian Rokuss, Lars Krämer +10
Accurate and efficient 3D segmentation is essential for both clinical and research applications. While foundation models like SAM have revolutionized interactive segmentation, thei…
Primus: Enforcing Attention Usage for 3D Medical Image Segmentation
Tassilo Wald, Saikat Roy, Fabian Isensee +7
Transformers have achieved remarkable success across multiple fields, yet their impact on 3D medical image segmentation remains limited with convolutional networks still dominating…
LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging
Maximilian Rokuss, Yannick Kirchhoff, Seval Akbal +7
In this work, we present LesionLocator, a framework for zero-shot longitudinal lesion tracking and segmentation in 3D medical imaging, establishing the first end-to-end model capab…
A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data
Michal Nohel, Constantin Ulrich, Jonathan Suprijadi +2
This study presents an open-source toolkit to address critical challenges in preprocessing data for self-supervised learning (SSL) for 3D medical imaging, focusing on data privacy…
Expectation-Maximization as the Engine of Scalable Medical Intelligence
Wenxuan Li, Pedro R. A. S. Bassi, Tianyu Lin +19
Large, high-quality, annotated datasets are the foundation of medical AI research, but constructing even a small, moderate-quality, annotated dataset can take years of effort from…