76 citations · 108 across the 16 of their papers we have counts for
18 papers
Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Space
Daniel Tweneboah Anyimadu, Mohammed Abdalla, Mohammed M. Abdelsamea +1
Low-field magnetic resonance imaging (MRI) offers a cost-effective alternative for medical imaging in resource-limited settings. However, its widespread adoption is hindered by two…
Low-Field Magnetic Resonance Image Quality Enhancement using Undersampled k-Space and Out-of-Distribution Generalisation
Daniel Tweneboah Anyimadu, Mohammed M. Abdelsamea, Ahmed Karam Eldaly
Low-field magnetic resonance imaging (MRI) offers affordable access to diagnostic imaging but faces challenges such as prolonged acquisition times and reduced image quality. Althou…
DeepHistoViT: An Interpretable Vision Transformer Framework for Histopathological Cancer Classification
Ravi Mosalpuri, Mohammed Abdelsamea, Ahmed Karam Eldaly
Histopathology remains the gold standard for cancer diagnosis because it provides detailed cellular-level assessment of tissue morphology. However, manual histopathological examina…
Causal Transfer in Medical Image Analysis
Mohammed M. Abdelsamea, Daniel Tweneboah Anyimadu, Tasneem Selim +4
Medical imaging models frequently fail when deployed across hospitals, scanners, populations, or imaging protocols due to domain shift, limiting their clinical reliability. While t…
Direct low-field MRI super-resolution using undersampled k-space
Daniel Tweneboah Anyimadu, Mohammed M. Abdelsamea, Ahmed Karam Eldaly
Low-field magnetic resonance imaging (MRI) provides affordable access to diagnostic imaging but suffers from prolonged acquisition and limited image quality. Accelerated imaging ca…
iPac: Incorporating Intra-image Patch Context into Graph Neural Networks for Medical Image Classification
Usama Zidan, Mohamed Gaber, Mohammed M. Abdelsamea
Graph neural networks have emerged as a promising paradigm for image processing, yet their performance in image classification tasks is hindered by a limited consideration of the u…
CURVETE: Curriculum Learning and Progressive Self-supervised Training for Medical Image Classification
Asmaa Abbas, Mohamed Gaber, Mohammed M. Abdelsamea
Identifying high-quality and easily accessible annotated samples poses a notable challenge in medical image analysis. Transfer learning techniques, leveraging pre-training data, of…
CAPRI-CT: Causal Analysis and Predictive Reasoning for Image Quality Optimization in Computed Tomography
Sneha George Gnanakalavathy, Hairil Abdul Razak, Robert Meertens +3
In computed tomography (CT), achieving high image quality while minimizing radiation exposure remains a key clinical challenge. This paper presents CAPRI-CT, a novel causal-aware d…
CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification
Asmaa Abbas, Mohamed Gaber, Mohammed M. Abdelsamea
Curriculum learning strategies have been proven to be effective in various applications and have gained significant interest in the field of machine learning. It has the ability to…
MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification
Zakaria Senousy, Mohammed M. Abdelsamea, Mohamed Medhat Gaber +4
Breast histology image classification is a crucial step in the early diagnosis of breast cancer. In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have demonst…
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection
Asmaa Abbas, Mohammed M. Abdelsamea, Mohamed Gaber
Due to the high availability of large-scale annotated image datasets, knowledge transfer from pre-trained models showed outstanding performance in medical image classification. How…
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
Asmaa Abbas, Mohammed M. Abdelsamea, Mohamed Medhat Gaber
Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets,…
Regional Active Contours based on Variational level sets and Machine Learning for Image Segmentation
M. Abdelsamea
Image segmentation is the problem of partitioning an image into different subsets, where each subset may have a different characterization in terms of color, intensity, texture, an…
An Effective Image Feature Classiffication using an improved SOM
M. Abdelsamea, Marghny H. Mohamed, Mohamed Bamatraf
Image feature classification is a challenging problem in many computer vision applications, specifically, in the fields of remote sensing, image analysis and pattern recognition. I…
An Automatic Seeded Region Growing for 2D Biomedical Image Segmentation
Mohammed M. Abdelsamea
In this paper, an automatic seeded region growing algorithm is proposed for cellular image segmentation. First, the regions of interest (ROIs) extracted from the preprocessed image…
Unsupervised Parallel Extraction based Texture for Efficient Image Representation
Mohammed M. Abdelsamea
SOM is a type of unsupervised learning where the goal is to discover some underlying structure of the data. In this paper, a new extraction method based on the main idea of Concurr…
Self Organization Map based Texture Feature Extraction for Efficient Medical Image Categorization
Marghny H. Mohamed, Mohammed M. Abdelsamea
Texture is one of the most important properties of visual surface that helps in discriminating one object from another or an object from background. The self-organizing map (SOM) i…
An Enhancement Neighborhood connected Segmentation for 2D-Cellular Image
Mohammed M. Abdelsamea
A good segmentation result depends on a set of "correct" choice for the seeds. When the input images are noisy, the seeds may fall on atypical pixels that are not representative of…