6 papers
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…
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…
Language and Planning in Robotic Navigation: A Multilingual Evaluation of State-of-the-Art Models
Malak Mansour, Ahmed Aly, Bahey Tharwat +3
Large Language Models (LLMs) such as GPT-4, trained on huge amount of datasets spanning multiple domains, exhibit significant reasoning, understanding, and planning capabilities ac…
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…