5 papers
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…
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…
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…
Improving Chest X-Ray Classification by RNN-based Patient Monitoring
David Biesner, Helen Schneider, Benjamin Wulff +2
Chest X-Ray imaging is one of the most common radiological tools for detection of various pathologies related to the chest area and lung function. In a clinical setting, automated…
Gradient Flows for L2 Support Vector Machine Training
Christian Bauckhage, Helen Schneider, Benjamin Wulff +1
We explore the merits of training of support vector machines for binary classification by means of solving systems of ordinary differential equations. We thus assume a continuous t…