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MedDIFT: Multi-Scale Diffusion-Based Correspondence in 3D Medical Imaging
Xingyu Zhang, Anna Reithmeir, Fryderyk Kögl +3
Accurate spatial correspondence between medical images is essential for longitudinal analysis, lesion tracking, and image-guided interventions. Medical image registration methods r…
LungEvaty: A Scalable, Open-Source Transformer-based Deep Learning Model for Lung Cancer Risk Prediction in LDCT Screening
Johannes Brandt, Maulik Chevli, Rickmer Braren +3
Lung cancer risk estimation is gaining increasing importance as more countries introduce population-wide screening programs using low-dose CT (LDCT). As imaging volumes grow, scala…
Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment
Dmitrii Seletkov, Sophie Starck, Ayhan Can Erdur +3
Reliable preclinical disease risk assessment is essential to move public healthcare from reactive treatment to proactive identification and prevention. However, image-based risk pr…
Parametric shape models for vessels learned from segmentations via differentiable voxelization
Alina F. Dima, Suprosanna Shit, Huaqi Qiu +8
Vessels are complex structures in the body that have been studied extensively in multiple representations. While voxelization is the most common of them, meshes and parametric mode…
On Arbitrary Predictions from Equally Valid Models
Sarah Lockfisch, Kristian Schwethelm, Martin Menten +4
Model multiplicity refers to the existence of multiple machine learning models that describe the data equally well but may produce different predictions on individual samples. In m…
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…
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…
PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI
Veronika Spieker, Hannah Eichhorn, Wenqi Huang +9
Neural implicit k-space representations (NIK) have shown promising results for dynamic magnetic resonance imaging (MRI) at high temporal resolutions. Yet, reducing acquisition time…