7 citations · 10 across the 13 of their papers we have counts for
5 papers · 1 filter
Beyond Predictive Fairness: Quantifying Attribution Consistency Across Demographic Groups in Diabetic Retinopathy Screening
Kerol Djoumessi, Philipp Berens
Fairness in medical imaging is commonly evaluated through subgroup performance metrics, yet it remains unclear whether models rely on consistent visual evidence across demographic…
Scientific Domain Knowledge Improves Vision-Language Fundus Models
Verena Jasmin Hallitschke, Carsten Eickhoff, Philipp Berens
Vision-language models hold considerable promise for ophthalmology, but it remains unclear which training data source best conveys expert domain knowledge. Existing ophthalmic mode…
TTE-CAM: Self-Explainable Class Activation Maps for Pretrained Black-Box CNNs
Kerol Djoumessi, Philipp Berens
Convolutional neural networks (CNNs) achieve state-of-the-art performance in medical image analysis yet remain opaque, limiting adoption in high-stakes clinical settings. Existing…
Towards Interpretable Foundation Models for Retinal Fundus Images
Samuel Ofosu Mensah, Camila Roa, Kerol Djoumessi +1
Foundation models are used to extract transferable representations from large amounts of unlabeled data, typically via self-supervised learning (SSL). However, many of these models…
Mitigating Shortcut Learning via Feature Disentanglement in Medical Imaging: A Benchmark Study
Sarah Müller, Philipp Berens
Although deep learning models in medical imaging often achieve excellent classification performance, they can rely on shortcut learning, exploiting spurious correlations or confoun…