Search Results for author: Essam A. Rashed

Found 15 papers, 6 papers with code

Neuro-TransUNet: Segmentation of stroke lesion in MRI using transformers

no code implementations10 Jun 2024 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 properties of the lesions.

Anatomy Lesion Segmentation +1

Transformers-based architectures for stroke segmentation: A review

no code implementations27 Mar 2024 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.

Computational Efficiency Segmentation

Brain Stroke Segmentation Using Deep Learning Models: A Comparative Study

no code implementations25 Mar 2024 Ahmed Soliman, Yousif Yousif, Ahmed Ibrahim, Yalda Zafari-Ghadim, Essam A. Rashed, Mohamed Mabrok

In this study, we selected four types of deep models that were recently proposed and evaluated their performance for stroke segmentation: a pure Transformer-based architecture (DAE-Former), two advanced CNN-based models (LKA and DLKA) with attention mechanisms in their design, an advanced hybrid model that incorporates CNNs with Transformers (FCT), and the well- known self-adaptive nnUNet framework with its configuration based on given data.

Image Segmentation Medical Image Segmentation +2

SHARM: Segmented Head Anatomical Reference Models

1 code implementation13 Sep 2023 Essam A. Rashed, Mohammad al-Shatouri, Ilkka Laakso, Akimasa Hirata

Reliable segmentation of anatomical tissues of human head is a major step in several clinical applications such as brain mapping, surgery planning and associated computational simulation studies.

Segmentation

COVID-19 forecasting using new viral variants and vaccination effectiveness models

no code implementations24 Jan 2022 Essam A. Rashed, Sachiko Kodera, Akimasa Hirata

Background: Recently, a high number of daily positive COVID-19 cases have been reported in regions with relatively high vaccination rates; hence, booster vaccination has become necessary.

Time Series Analysis

Influence of segmentation accuracy in structural MR head scans on electric field computation for TMS and tES

no code implementations25 Sep 2020 Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata

In brain, sensitivity to segmentation accuracy is relatively high in cerebrospinal fluid (CSF), moderate in gray matter (GM) and low in white matter for transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES).

Open-Ended Question Answering Segmentation

Deep learning approach for breast cancer diagnosis

no code implementations10 Mar 2020 Essam A. Rashed, M. Samir Abou El Seoud

Breast cancer is one of the leading fatal disease worldwide with high risk control if early discovered.

Decision Making Specificity

Development of accurate human head models for personalized electromagnetic dosimetry using deep learning

1 code implementation21 Feb 2020 Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata

However, most studies have focused on the segmentation of brain tissue only and little attention has been paid to other tissues, which are considerably important for electromagnetic dosimetry.

Segmentation

End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation

1 code implementation13 Feb 2020 Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata

However, it is difficult to determine the amount and distribution of the electric field (EF) in the different brain regions due to anatomical complexity and high inter-subject variability.

Brain Segmentation Semantic Segmentation

Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry

1 code implementation4 Nov 2019 Essam A. Rashed, Yinliang Diao, Akimasa Hirata

The smooth distribution of the dielectric properties in head models, which is realized using a process without tissue segmentation, improves the smoothness of the specific absorption rate (SAR) distribution compared with that in the commonly used procedure.

Segmentation

Deep learning-based development of personalized human head model with non-uniform conductivity for brain stimulation

1 code implementation6 Oct 2019 Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata

This paper proposes a novel approach to the automatic estimation of electric conductivity in the human head for volume conductor models without anatomical segmentation.

Segmentation

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