12 citations · 17 across the 8 of their papers we have counts for
8 papers
TRACE: A Concept Bottleneck Model for Longitudinal 3D Glioblastoma Response Assessment
Alia Tarek, Hamsa Saberr, Hamza Elghonemy +6
Longitudinal glioblastoma response assessment requires comparing subtle tumor changes across MRI time points using structured clinical criteria such as RANO. However, most deep lea…
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…
InFL-UX: A Toolkit for Web-Based Interactive Federated Learning
Tim Maurer, Abdulrahman Mohamed Selim, Hasan Md Tusfiqur Alam +3
This paper presents InFL-UX, an interactive, proof-of-concept browser-based Federated Learning (FL) toolkit designed to integrate user contributions seamlessly into the machine lea…
Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System
Devansh Srivastav, Hasan Md Tusfiqur Alam, Afsaneh Asaei +3
Efficient online learning requires seamless access to diverse resources such as videos, code repositories, documentation, and general web content. This poster paper introduces earl…
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…
Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends
Duy M. H. Nguyen, Hasan Md Tusfiqur Alam, Tai Nguyen +4
The emergence of artificial intelligence (AI), particularly deep learning (DL), has marked a new era in the realm of ophthalmology, offering transformative potential for the diagno…