collaborators

5 papers

math.RA2026

The zero divisor and augmentation conjectures for medial quandle rings

Luc Ta

For quandle rings, Bardakov--Passi--Singh and Elhamdadi--Nunez--Singh respectively formulated an analogue of Kaplansky's zero divisor conjecture and a weakened analogue of Kaplansk…

cs.CV2026

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…

cs.LG2025

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…

cs.CV2025

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…

stat.ML2025

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…