6 citations · 19 across the 21 of their papers we have counts for
3 papers · 1 filter
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…
From Retinal Pixels to Patients: Evolution of Deep Learning Research in Diabetic Retinopathy Screening
Muskaan Chopra, Lorenz Sparrenberg, Armin Berger +3
Diabetic Retinopathy (DR) remains a leading cause of preventable blindness, with early detection critical for reducing vision loss worldwide. Over the past decade, deep learning ha…
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…