Sven Nebelung

Orcid: 0000-0002-5267-9962

According to our database1, Sven Nebelung authored at least 28 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Encrypted federated learning for secure decentralized collaboration in cancer image analysis.
Medical Image Anal., February, 2024

Medical large language models are susceptible to targeted misinformation attacks.
npj Digit. Medicine, 2024

RadioRAG: Factual Large Language Models for Enhanced Diagnostics in Radiology Using Dynamic Retrieval Augmented Generation.
CoRR, 2024

Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization.
CoRR, 2024

An Ordinal Regression Framework for a Deep Learning Based Severity Assessment for Chest Radiographs.
CoRR, 2024

On Instabilities of Unsupervised Denoising Diffusion Models in Magnetic Resonance Imaging Reconstruction.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

2023
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models.
CoRR, 2023

Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative Pretraining.
CoRR, 2023

Medical Foundation Models are Susceptible to Targeted Misinformation Attacks.
CoRR, 2023

Empowering Clinicians and Democratizing Data Science: Large Language Models Automate Machine Learning for Clinical Studies.
CoRR, 2023

Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images.
CoRR, 2023

Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy.
CoRR, 2023

Fibroglandular Tissue Segmentation in Breast MRI using Vision Transformers - A multi-institutional evaluation.
CoRR, 2023

Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology.
CoRR, 2023

Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers.
Proceedings of the Machine Learning in Medical Imaging - 14th International Workshop, 2023

Transformers for CT Reconstruction from Monoplanar and Biplanar Radiographs.
Proceedings of the Simulation and Synthesis in Medical Imaging, 2023

Multi-View Abnormality Detection in Clinical Knee MRI Studies Using Transformers.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

Vector-Quantized Latent Flows for Medical Image Synthesis and Out-Of-Distribution Detection.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

2022
Image prediction of disease progression for osteoarthritis by style-based manifold extrapolation.
Nat. Mac. Intell., November, 2022

Medical Diagnosis with Large Scale Multimodal Transformers: Leveraging Diverse Data for More Accurate Diagnosis.
CoRR, 2022

Diffusion Probabilistic Models beat GANs on Medical Images.
CoRR, 2022

Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels.
CoRR, 2022

Medical Diffusion - Denoising Diffusion Probabilistic Models for 3D Medical Image Generation.
CoRR, 2022

Monoplanar CT Reconstruction with GANs.
Proceedings of the Eleventh International Conference on Image Processing Theory, 2022

2021
Predicting Osteoarthritis Progression in Radiographs via Unsupervised Representation Learning.
CoRR, 2021

A Novel Combined Level Set Model for Carpus Segmentation from Magnetic Resonance Images with Prior Knowledge aligned in Polar Coordinate System.
Comput. Methods Programs Biomed., 2021

2020
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization.
CoRR, 2020

A Method for Semantic Knee Bone and Cartilage Segmentation with Deep 3D Shape Fitting Using Data from the Osteoarthritis Initiative.
Proceedings of the Shape in Medical Imaging - International Workshop, 2020


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