7 citations · 10 across the 13 of their papers we have counts for
13 papers
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…
Uncertainty-Aware Retinal Vessel Segmentation via Ensemble Distillation
Jeremiah Fadugba, Petru Manescu, Bolanle Oladejo +2
Uncertainty estimation is critical for reliable medical image segmentation, particularly in retinal vessel analysis, where accurate predictions are essential for diagnostic applica…
Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning
Samuel Ofosu Mensah, Kerol Djoumessi, Philipp Berens
Federated learning (FL) provides a promising paradigm for collaboratively training machine learning models across distributed data sources while maintaining privacy. Nevertheless,…
Soft-CAM: Making black box models self-explainable for medical image analysis
Kerol Djoumessi, Philipp Berens
Convolutional neural networks (CNNs) are widely used for high-stakes applications like medicine, often surpassing human performance. However, most explanation methods rely on post-…
A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Disease Detection from Retinal Fundus Images
Kerol Djoumessi, Samuel Ofosu Mensah, Philipp Berens
In many medical imaging tasks, convolutional neural networks (CNNs) efficiently extract local features hierarchically. More recently, vision transformers (ViTs) have gained popular…
Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging
Sarah Müller, Louisa Fay, Lisa M. Koch +3
Medical imaging cohorts are often confounded by factors such as acquisition devices, hospital sites, patient backgrounds, and many more. As a result, deep learning models tend to l…
This actually looks like that: Proto-BagNets for local and global interpretability-by-design
Kerol Djoumessi, Bubacarr Bah, Laura Kühlewein +2
Interpretability is a key requirement for the use of machine learning models in high-stakes applications, including medical diagnosis. Explaining black-box models mostly relies on…
Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization
Jeremiah Fadugba, Patrick Köhler, Lisa Koch +2
Retinal blood vessel segmentation can extract clinically relevant information from fundus images. As manual tracing is cumbersome, algorithms based on Convolution Neural Networks h…
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
Jonathan Fuhr, Philipp Berens, Dominik Papies
The estimation of causal effects with observational data continues to be a very active research area. In recent years, researchers have developed new frameworks which use machine l…