Multilinguality and Language Technology

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1. Hasan Md Tusfiqur Alam; Devansh Srivastav; Abdulrahman Mohamed Selim; Md Abdul Kadir; Md Moktadirul Hoque Shuvo; Daniel Sonntag

   CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models

   In: Companion Proceedings of the 17th ACM SIGCHI Symposium on Engineering Interactive Computing Systems. ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS-2025), 17th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, located at EICS-2025, June 23-27, Trier, Germany, Pages 59-61, EICS '25 Companion (EICS '25 Companion), ISBN 9798400718663, Association for Computing Machinery, 6/2025.

2. Hasan Md Tusfiqur Alam; Devansh Srivastav; Md Abdul Kadir; Daniel Sonntag

   Towards Interpretable Radiology Report Generation via Concept Bottlenecks Using a Multi-agentic RAG

   In: Claudia Hauff; Craig Macdonald; Dietmar Jannach; Gabriella Kazai; Franco Maria Nardini; Fabio Pinelli; Fabrizio Silvestri; Nicola Tonellotto (Hrsg.). Advances in Information Retrieval - 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6-10, 2025, Proceedings, Part III. European Conference on Information Retrieval (ECIR-2025), 47th European Conference on Information Retrieval, located at ECIR 2025, April 6-10, Lucca, Italy, Pages 201-209, Lecture Notes in Computer Science, Vol. 15574, Springer, 4/2025.

3. Md Abdul Kadir; Hasan Md Tusfiqur Alam; Devansh Srivastav; Hans-Jürgen Profitlich; Daniel Sonntag

   Partial Image Active Annotation (PIAA): An Efficient Active Learning Technique Using Edge Information in Limited Data Scenarios

   In: KI - Künstliche Intelligenz, German Journal on Artificial Intelligence - Organ des Fachbereiches "Künstliche Intelligenz" der Gesellschaft für Informatik e.V. (KI), Pages 1-12, Springer, 6/2024.

4. Md Abdul Kadir; Hasan Md Tusfiqur Alam; Pascale Maul; Hans-Jürgen Profitlich; Moritz Wolf; Daniel Sonntag

   Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project

   3/2024.

5. Md Abdul Kadir; Gowthamkrishna Addluri; Daniel Sonntag

   Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors

   3/2024.

6. Md Abdul Kadir; Abdulrahman Mohamed Selim; Michael Barz; Daniel Sonntag

   A User Interface for Explaining Machine Learning Model Explanations

   In: Companion Proceedings of the 28th International Conference on Intelligent User Interfaces. International Conference on Intelligent User Interfaces (IUI-2023), March 27-31, Sydney, NSW, Australia, Pages 59-63, IUI'23 Companion, Vol. Companion Proceedings of the 28th International Conference on Intelligent User Interfaces, ISBN 9798400701078, Association for Computing Machinery, New York, NY, United States, 3/2023.

7. Md Abdul Kadir; Gowthamkrishna Addluri; Daniel Sonntag

   Harmonizing Feature Attributions Across Deep Learning Architectures: Enhancing Interpretability and Consistency

   In: Dietmar Seipel; Alexander Steen. German Conference on Artificial Intelligence. Pages 90-97, Lecture Notes in Computer Science, ISBN 978-3-031-42607-0, Springer, Cham, 2023.

8. Md Abdul Kadir; Hasan Md Tusfiqur Alam; Daniel Sonntag

   EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation

   In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Medical Image Computing and Computer Assisted Intervention (MICCAI-2023), Pages 79-89, Springer, Cham, 2023.

9. Md Abdul Kadir; Amir Mosavi; Daniel Sonntag

   Evaluation Metrics for XAI: A Review, Taxonomy, and Practical Applications

   In: 2023 IEEE 27th International Conference on Intelligent Engineering Systems (INES). Conference on Intelligent Engineering Systems (INES-2023), July 26-28, Nairobi, Kenya, Pages 000111-000124, IEEE, 2023.

10. Md Abdul Kadir; Fabrizio Nunnari; Daniel Sonntag

    Fine-tuning of explainable CNNs for skin lesion classification based on dermatologists' feedback towards increasing trust

    2023.

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