5 papers
Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentation
Simon Baur, Arne Schernich, Ekin Böke +2
Uncertainty estimation is critical for the safe clinical deployment of deep learning in medical image segmentation, with aleatoric uncertainty theoretically designed to capture irr…
Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification
Simon Baur, Wojciech Samek, Jackie Ma
Reliable uncertainty quantification is crucial for trustworthy decision-making and the deployment of AI models in medical imaging. While prior work has explored the ability of neur…
Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors Undergoing 177Lu-based Peptide Receptor Radionuclide Therapy
Simon Baur, Tristan Ruhwedel, Ekin Böke +11
Peptide receptor radionuclide therapy (PRRT) is an established treatment for metastatic neuroendocrine tumors (NETs), yet long-term disease control occurs only in a subset of patie…
Fractional Diffusion Bridge Models
Gabriel Nobis, Maximilian Springenberg, Arina Belova +5
We present Fractional Diffusion Bridge Models (FDBM), a novel generative diffusion bridge framework driven by an approximation of the rich and non-Markovian fractional Brownian mot…
Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEs
Jost Arndt, Utku Isil, Michael Detzel +2
Many physical processes can be expressed through partial differential equations (PDEs). Real-world measurements of such processes are often collected at irregularly distributed poi…