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…
MADE: A Living Benchmark for Multi-Label Text Classification with Uncertainty Quantification of Medical Device Adverse Events
Raunak Agarwal, Markus Wenzel, Simon Baur +3
Machine learning in high-stakes domains such as healthcare requires not only strong predictive performance but also reliable uncertainty quantification (UQ) to support human oversi…
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…
On the effectiveness of multimodal privileged knowledge distillation in two vision transformer based diagnostic applications
Simon Baur, Alexandra Benova, Emilio Dolgener Cantú +1
Deploying deep learning models in clinical practice often requires leveraging multiple data modalities, such as images, text, and structured data, to achieve robust and trustworthy…