4 papers
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…