8 papers
MIFR: A Modality-Invariant and Fair Representation Framework for Skin Disease Classification
Asonyu Senge Njih, Yvan Guifo Fodjo, Vianney Kengne Tchendji +2
Skin diseases represent a major global public health burden, yet machine learning tools developed to assist in their diagnosis suffer from two critical limitations: reliance on onl…
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…
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…
Beyond Accuracy: Evaluating Efficiency, Robustness and Explainability in Deep Learning for Malaria Diagnosis
Olivier Kanamugire, Kerol Djoumessi
Malaria remains a leading cause of mortality in sub-Saharan Africa, where scarce diagnostic infrastructure makes timely, accurate diagnosis particularly challenging. While deep lea…
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…
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-…