On the Overlap Between Grad-CAM Saliency Maps and Explainable Visual Features in Skin Cancer Images. - Fabrizio Nunnari; Md Abdul Kadir; Daniel Sonntag (CD-MAKE, 2021)

A User Interface for Explaining Machine Learning Model Explanations. - Md Abdul Kadir; Abdulrahman Mohamed Selim; Michael Barz; Daniel Sonntag (IUI Companion, 2023)

Harmonizing Feature Attributions Across Deep Learning Architectures: Enhancing Interpretability and Consistency. - Md Abdul Kadir; Gowtham Krishna Addluri; Daniel Sonntag (KI, 2023)

EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation. - Md Abdul Kadir; Hasan Md Tusfiqur Alam; Daniel Sonntag (MICCAI (2), 2023)

Fine-tuning of explainable CNNs for skin lesion classification based on dermatologists' feedback towards increasing trust. - Md Abdul Kadir; Fabrizio Nunnari; Daniel Sonntag (CoRR, 2023)

Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project. - Md Abdul Kadir; Hasan Md Tusfiqur Alam; Pascale Maul; Hans-Jürgen Profitlich; Moritz Wolf; Daniel Sonntag (CoRR, 2024)

Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors. - Md Abdul Kadir; Gowtham Krishna Addluri; Daniel Sonntag (CoRR, 2024)

Partial Image Active Annotation (PIAA): An Efficient Active Learning Technique Using Edge Information in Limited Data Scenarios. - Md Abdul Kadir; Hasan Md Tusfiqur Alam; Devansh Srivastav; Hans-Jürgen Profitlich; Daniel Sonntag (Künstliche Intell., 2024)

Towards Interpretable Radiology Report Generation via Concept Bottlenecks Using a Multi-agentic RAG. - Hasan Md Tusfiqur Alam; Devansh Srivastav; Md Abdul Kadir; Daniel Sonntag (ECIR (3), 2025)

CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models. - Hasan Md Tusfiqur Alam; Devansh Srivastav; Abdulrahman Mohamed Selim; Md Abdul Kadir; Md Moktadirul Hoque Shuvo; Daniel Sonntag (EICS (Companion), 2025)