12 citations · 20 across the 9 of their papers we have counts for
10 papers
What Do Visual-Head Discovery Scores Actually Find? Attention Source, Necessity, and Random-Control Pitfalls in Vision-Language Models
Md Abdul Kadir, Daniel Sonntag
Attention heads that attend to image regions relevant to a question are increasingly used to steer, ground and interpret vision-language models (VLMs) and to reduce their hallucina…
Beyond Heatmaps: Unsupervised Concept-Graph Reasoning for Interpretable Visual Explanation
Md Mohasin Hossain, Anar Amirli, Robert Leist +2
Concept Bottleneck Models (CBMs) provide an intrinsically interpretable alternative to post-hoc explanations. However, existing CBMs often rely on predefined concept vocabularies o…
AuditCopilot: Leveraging LLMs for Fraud Detection in Double-Entry Bookkeeping
Md Abdul Kadir, Sai Suresh Macharla Vasu, Sidharth S. Nair +1
Auditors rely on Journal Entry Tests (JETs) to detect anomalies in tax-related ledger records, but rule-based methods generate overwhelming false positives and struggle with subtle…
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 +3
Advancements in generative Artificial Intelligence (AI) hold great promise for automating radiology workflows, yet challenges in interpretability and reliability hinder clinical ad…
Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAG
Hasan Md Tusfiqur Alam, Devansh Srivastav, Md Abdul Kadir +1
Deep learning has advanced medical image classification, but interpretability challenges hinder its clinical adoption. This study enhances interpretability in Chest X-ray (CXR) cla…
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors
Md Abdul Kadir, GowthamKrishna Addluri, Daniel Sonntag
Explainable Artificial Intelligence (XAI) strategies play a crucial part in increasing the understanding and trustworthiness of neural networks. Nonetheless, these techniques could…
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 +3
Image annotation is one of the most essential tasks for guaranteeing proper treatment for patients and tracking progress over the course of therapy in the field of medical imaging…
EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation
Md Abdul Kadir, Hasan Md Tusfiqur Alam, Daniel Sonntag
Active learning algorithms have become increasingly popular for training models with limited data. However, selecting data for annotation remains a challenging problem due to the l…
Harmonizing Feature Attributions Across Deep Learning Architectures: Enhancing Interpretability and Consistency
Md Abdul Kadir, Gowtham Krishna Addluri, Daniel Sonntag
Ensuring the trustworthiness and interpretability of machine learning models is critical to their deployment in real-world applications. Feature attribution methods have gained sig…
Fine-tuning of explainable CNNs for skin lesion classification based on dermatologists' feedback towards increasing trust
Md Abdul Kadir, Fabrizio Nunnari, Daniel Sonntag
In this paper, we propose a CNN fine-tuning method which enables users to give simultaneous feedback on two outputs: the classification itself and the visual explanation for the cl…