131 citations · 580 across the 25 of their papers we have counts for
9 papers · 1 filter
Energy-Mamba: A Physics-Constrained State-Space Model for Medical Image Classification
Mohamed Mabrok, Yalda Zafari, Essam A. Rashed
State-Space Models (SSMs), particularly Mamba, offer linear-time complexity for long-range dependencies, making them attractive for medical imaging with limited annotated data. How…
MammoClean: Toward Reproducible and Bias-Aware AI in Mammography through Dataset Harmonization
Yalda Zafari, Hongyi Pan, Gorkem Durak +3
The development of clinically reliable artificial intelligence (AI) systems for mammography is hindered by profound heterogeneity in data quality, metadata standards, and populatio…
A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusion
Yalda Zafari, Roaa Elalfy, Mohamed Mabrok +3
Early and accurate interpretation of screening mammograms is essential for effective breast cancer detection, yet it remains a complex challenge due to subtle imaging findings and…
Neuro-TransUNet: Segmentation of stroke lesion in MRI using transformers
Muhammad Nouman, Mohamed Mabrok, Essam A. Rashed
Accurate segmentation of the stroke lesions using magnetic resonance imaging (MRI) is associated with difficulties due to the complicated anatomy of the brain and the different pro…
Transformers-based architectures for stroke segmentation: A review
Yalda Zafari-Ghadim, Essam A. Rashed, Mohamed Mabrok
Stroke remains a significant global health concern, necessitating precise and efficient diagnostic tools for timely intervention and improved patient outcomes. The emergence of dee…
Deep models for stroke segmentation: do complex architectures always perform better?
Yalda Zafari-Ghadim, Ahmed Soliman, Yousif Yousif +3
Stroke segmentation plays a crucial role in the diagnosis and treatment of stroke patients by providing spatial information about affected brain regions and the extent of damage. S…