Scientific Publications

The following scientific works and research publications have been developed in the framework of the iToBoS project:

• Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy.
• Towards the Interpretability of Deep Learning Models for Human Neuroimaging.
• Finding and removing Clever Hans: Using explanation methods to debug and improve deep models.
• Explain and improve: LRP-inference fine-tuning for image captioning models.
• PatClarC: Using pattern concept activation vectors for noise-robust model debugging.
• Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations.
• Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement.
• ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs.
• Explaining Machine Learning Models for Clinical Gait Analysis.
• Explainable AI Methods - A Brief Overview.
• Explaining the Predictions of Unsupervised Learning Models.
• CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations.
• Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI.
• Registration of polarimetric images for in vivo skin diagnostics.
• Focus stacking in non-contact dermoscopy.
• Measurably Stronger Explanation Reliability via Model Canonization.
• What to Hide from Your Students: Attention-Guided Masked Image Modeling.
• Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations.
• Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations.
• Impact of standardization in tissue processing: the performance of different fixatives.
• Optimizing Explanations by Network Canonization and Hyperparameter Search.
• Mueller Matrix Microscopy for In Vivo Scar Tissue Diagnostics and Treatment Evaluation.
• The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus.
• AI privacy toolkit.
• Generative Adversarial Network for Personalized Art Therapy in Melanoma Disease Management.
• Explainable AI for Time Series via Virtual Inspection Layers.
• Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models.
• Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence.
• xxAI - Beyond Explainable Artificial Intelligence.
• Association of germline variants in telomere maintenance genes (POT1, TERF2IP, ACD, and TERT) with spitzoid morphology in familial melanoma: A multi-center case series.
• A group of three miRNAs can act as candidate circulating biomarkers in liquid biopsies from melanoma patients.