most citedFrom Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research

16 citations · 23 across the 10 of their papers we have counts for

collaborators

10 papers

q-bio.NC2026

From Observation to Intervention: Memory in Brains and Large Language Models

Morteza Salehjahromi, Shayan A. Zadegan, Amgad Muneer +1

Brains and large language models (LLMs) are fundamentally different memory systems, but they can be compared through shared functional questions: where memory-related information i…

cs.CV2026

Towards Responsible Multimodal Medical Reasoning via Context-Aligned Vision-Language Models

Sumra Khan, Sagar Chhabriya, Aizan Zafar +5

Medical vision-language models (VLMs) show strong performance on radiology tasks but often produce fluent yet weakly grounded conclusions due to over-reliance on a dominant modalit…

cs.CL2026

Temperature-Dependent Performance of Prompting Strategies in Extended Reasoning Large Language Models

Mousa Salah, Amgad Muneer

Extended reasoning models represent a transformative shift in Large Language Model (LLM) capabilities by enabling explicit test-time computation for complex problem solving. Howeve…

eess.IV2026★ 1 cited

Projection Guided Personalized Federated Learning for Low Dose CT Denoising

Anas Zafar, Muhammad Waqas, Amgad Muneer +2

Low-dose CT (LDCT) reduces radiation exposure but introduces protocol-dependent noise and artifacts that vary across institutions. While federated learning enables collaborative tr…

cs.CV2026

CARL-CXR: Continual Adapter-Based Routing for Task-Unknown Chest Radiograph Classification

Muthu Subash Kavitha, Anas Zafar, Amgad Muneer +1

Clinical deployment of chest radiograph classifiers requires models that can be updated as new datasets become available without retraining on previously observed data or degrading…

q-bio.QM2025

Foundation Models in Biomedical Imaging: Turning Hype into Reality

Amgad Muneer, Kai Zhang, Ibraheem Hamdi +6

Foundation models (FMs) are driving a prominent shift in biomedical imaging from task-specific models to unified backbone models for diverse tasks. This opens an avenue to integrat…

q-bio.QM2025

The Next Layer: Augmenting Foundation Models with Structure-Preserving and Attention-Guided Learning for Local Patches to Global Context Awareness in Computational Pathology

Muhammad Waqas, Rukhmini Bandyopadhyay, Eman Showkatian +12

Foundation models have recently emerged as powerful feature extractors in computational pathology, yet they typically omit mechanisms for leveraging the global spatial structure of…

cs.AI2025★ 1 cited

Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact

Rizwan Qureshi, Ranjan Sapkota, Abbas Shah +16

Can machines truly think, reason and act in domains like humans? This enduring question continues to shape the pursuit of Artificial General Intelligence (AGI). Despite the growing…

q-bio.QM2025★ 16 cited

From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research

Amgad Muneer, Muhammad Waqas, Maliazurina B Saad +16

Cancer research is increasingly driven by the integration of diverse data modalities, spanning from genomics and proteomics to imaging and clinical factors. However, extracting act…

cs.CY2025★ 5 cited

Who is Responsible? The Data, Models, Users or Regulations? A Comprehensive Survey on Responsible Generative AI for a Sustainable Future

Shaina Raza, Rizwan Qureshi, Anam Zahid +14

Generative AI is rapidly moving from research to deployment, elevating the need for responsible development, evaluation, and governance. We conduct a PRISMA guided review of 232 st…