13 papers
Carve VFM: Carving Specialists out of Generalists, Automatic Task-Specific Compression of Vision Foundation Models
Lotfi Abdelkrim Mecharbat, Abdelrahman Elsayed, Ibrahim Almakky +2
Vision foundation models (VFMs) provide transferable visual representations, but their inference cost limits deployment in many settings. Fine-tuning specializes a VFM without shri…
Noise Out, Bias In: Targeted Bias Injection in Diffusion Language Models via Closed-Loop Activation Steering
Sarim Hashmi, Mukul Ranjan, Abdelrahman Elsayed +3
Masked diffusion language models (dLLMs) generate text by iteratively denoising masked positions, re-predicting each token multiple times before it is committed. An autoregressive…
Certification of Real Images through Calibrated Content Authentication
Sarim Hashmi, Abdelrahman Elsayed, Mohammed Talha Alam +2
Generative models can synthesize high-quality inauthentic multimedia content that is already being misused at scale. We evaluate twenty deepfake detectors against ten generators re…
Quantum PDE Solvers in Practice: Application-Driven Benchmarking of the Heat Equation
Mahmoud Elkarargy, Abdelaziz Rahwan, Abdelrahman Elsayed +1
Quantum PDE solvers are difficult to evaluate in practice because published studies use different discretizations, output models, reconstruction rules, and hardware assumptions. We…
Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery
Amir H. M. Labeb, Basmala Sallam, Abdelrahman W. Elsayed +3
Physics-informed neural networks (PINNs) constitute a rapidly maturing class of scientific machine learning models in which the governing equations of a physical system are embedde…
Robust and Calibrated Detection of Authentic Multimedia Content
Sarim Hashmi, Abdelrahman Elsayed, Mohammed Talha Alam +2
Generative models can synthesize highly realistic content, so-called deepfakes, that are already being misused at scale to undermine digital media authenticity. Current deepfake de…
MMRINet: Efficient Mamba-Based Segmentation with Dual-Path Refinement for Low-Resource MRI Analysis
Abdelrahman Elsayed, Ahmed Jaheen, Mohammad Yaqub
Automated brain tumor segmentation in multi-parametric MRI remains a critical yet underserved challenge in resource-constrained clinical settings, where deep 3D networks requiring…
EgMM-Corpus: A Multimodal Vision-Language Dataset for Egyptian Culture
Mohamed Gamil, Abdelrahman Elsayed, Abdelrahman Lila +4
Despite recent advances in AI, multimodal culturally diverse datasets are still limited, particularly for regions in the Middle East and Africa. In this paper, we introduce EgMM-Co…
Terahertz Quasi-BIC Metasurfaces for Ultra-Sensitive Biosensing and High-Speed Wireless Communications
Islam I. Abdulaal, Abdelrahman W. A. Elsayed, Omar A. M. Abdelraouf
Bound states in the continuum (BICs) have emerged as a revolutionary paradigm in terahertz (THz) photonics, enabling metasurfaces with theoretically infinite quality factors (Q-fac…
Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach
Daniil Tikhonov, Matheus Scatolin, Mohor Banerjee +7
Accurate evaluation of the response of glioblastoma to therapy is crucial for clinical decision-making and patient management. The Response Assessment in Neuro-Oncology (RANO) crit…
EMedNeXt: An Enhanced Brain Tumor Segmentation Framework for Sub-Saharan Africa using MedNeXt V2 with Deep Supervision
Ahmed Jaheen, Abdelrahman Elsayed, Damir Kim +8
Brain cancer affects millions worldwide, and in nearly every clinical setting, doctors rely on magnetic resonance imaging (MRI) to diagnose and monitor gliomas. However, the curren…
SALT: Parameter-Efficient Fine-Tuning via Singular Value Adaptation with Low-Rank Transformation
Abdelrahman Elsayed, Sarim Hashmi, Mohammed Elseiagy +3
The complex nature of medical image segmentation calls for models that are specifically designed to capture detailed, domain-specific features. Large foundation models offer consid…
Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics
Sarim Hashmi, Juan Lugo, Abdelrahman Elsayed +6
Identifying key pathological features in brain MRIs is crucial for the long-term survival of glioma patients. However, manual segmentation is time-consuming, requiring expert inter…