3 papers
cs.CV2026
Generalizing Abstention for Noise-Robust Learning in Medical Image Segmentation
Wesam Moustafa, Hossam Elsafty, Helen Schneider +2
Label noise is a critical problem in medical image segmentation, often arising from the inherent difficulty of manual annotation. Models trained on noisy data are prone to overfitt…
cs.AI2026
Benchmark Success, Clinical Failure: When Reinforcement Learning Optimizes for Benchmarks, Not Patients
Armin Berger, Manuela Bergau, Helen Schneider +7
Recent Reinforcement Learning (RL) advances for Large Language Models (LLMs) have improved reasoning tasks, yet their resource-constrained application to medical imaging remains un…
cs.CV2024
Informed Deep Abstaining Classifier: Investigating noise-robust training for diagnostic decision support systems
Helen Schneider, Sebastian Nowak, Aditya Parikh +6
Image-based diagnostic decision support systems (DDSS) utilizing deep learning have the potential to optimize clinical workflows. However, developing DDSS requires extensive datase…