Publications (16)
CheXplaining in Style: Counterfactual Explanations for Chest X-rays using StyleGAN
Matan Atad, Vitalii Dmytrenko, Yitong Li +6
Deep learning models used in medical image analysis are prone to raising reliability concerns due to their black-box nature. To shed light on these black-box models, previous works…
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning
Matan Atad, Jianxiang Feng, Ismael RodrÃguez +2
Automatic Robotic Assembly Sequence Planning (RASP) can significantly improve productivity and resilience in modern manufacturing along with the growing need for greater product cu…
VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank
Robert Graf, Paul-Sören Platzek, Evamaria Olga Riedel +17
Objectives: To present a publicly available deep learning-based torso segmentation model that provides comprehensive voxel-wise coverage, including delineations that extend to the…
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder
Matan Atad, David Schinz, Hendrik Moeller +6
Counterfactual explanations (CEs) aim to enhance the interpretability of machine learning models by illustrating how alterations in input features would affect the resulting predic…
Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading
Matthias Keicher, Matan Atad, David Schinz +9
Vertebral fractures are a consequence of osteoporosis, with significant health implications for affected patients. Unfortunately, grading their severity using CT exams is hard and…
VERIDAH: Solving Enumeration Anomaly Aware Vertebra Labeling across Imaging Sequences
Hendrik Möller, Hanna Schoen, Robert Graf +14
The human spine commonly consists of seven cervical, twelve thoracic, and five lumbar vertebrae. However, enumeration anomalies may result in individuals having eleven or thirteen…
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…
Hide-and-Seek Attribution: Weakly Supervised Segmentation of Vertebral Metastases in CT
Matan Atad, Alexander W. Marka, Lisa Steinhelfer +10
Accurate segmentation of vertebral metastasis in CT is clinically important yet difficult to scale, as voxel-level annotations are scarce and both lytic and blastic lesions often r…
One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation
Nathan Molinier, Hendrik Möller, Thomas Dagonneau +6
Deep learning-based medical image segmentation is increasingly used to support clinical diagnosis and develop new treatment strategies. However, model performance remains limited b…
Neural Network Surrogate and Projected Gradient Descent for Fast and Reliable Finite Element Model Calibration: a Case Study on an Intervertebral Disc
Matan Atad, Gabriel Gruber, Marx Ribeiro +7
Accurate calibration of finite element (FE) models is essential across various biomechanical applications, including human intervertebral discs (IVDs), to ensure their reliability…
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
Jianxiang Feng, Matan Atad, Ismael RodrÃguez +3
Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be introspective on the predicted solutions, i.e. whether they are feasible or not, to circumvent…
Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings using Spine MRI data
Robert Graf, Florian Hunecke, Soeren Pohl +12
Deep learning has made significant strides in medical imaging, leveraging the use of large datasets to improve diagnostics and prognostics. However, large datasets often come with…
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…
SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation
Hendrik Möller, Robert Graf, Joachim Schmitt +16
Purpose. To present SPINEPS, an open-source deep learning approach for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs,…
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…
PARASIDE: An Automatic Paranasal Sinus Segmentation and Structure Analysis Tool for MRI
Hendrik Möller, Lukas Krautschick, Matan Atad +9
Chronic rhinosinusitis (CRS) is a common and persistent sinus imflammation that affects 5 - 12\% of the general population. It significantly impacts quality of life and is often di…