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20182026
most citedTransformers-based architectures for stroke segmentation: A review

18 citations · 25 across the 12 of their papers we have counts for

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7 papers · 1 filter

eess.IV2026

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…

eess.IV2026

BreastMammo and DenseMammo: Benchmarks for Mammography Domain Generalization

Hongyi Pan, Gorkem Durak, Halil Ertugrul Aktas +18

Breast density classification is a critical component of breast cancer risk assessment, yet AI models often struggle to generalize across clinical sites due to vendor-specific acqu…

eess.IV2025

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…

eess.IV2025

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…

eess.IV2024★ 4 cited

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…

eess.IV2024★ 18 cited

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…

eess.IV2024★ 1 cited

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…