ORCID Profile
0000-0002-2728-1127
Current Organisations
Assiut University
,
University of Nottingham
,
Birmingham City University
,
University of Exeter
,
University of Warwick Warwick Medical School
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Publications
A Concurrent SOM-Based Chan-Vese Model for Image Segmentation
Publisher: Springer International Publishing
Date: 2014
MCUa: Multi-Level Context and Uncertainty Aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
DeTrac: Transfer Learning of Class Decomposed Medical Images in Convolutional Neural Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images
Publisher: MDPI AG
Date: 16-05-2021
DOI: 10.3390/E23050620
Abstract: Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%.
An efficient Self-Organizing Active Contour model for image segmentation
Publisher: Elsevier BV
Date: 02-2015
A survey on artificial intelligence in histopathology image analysis
Publisher: Wiley
Date: 27-07-2022
DOI: 10.1002/WIDM.1474
Abstract: The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning‐based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field. This article is categorized under: Application Areas Health Care
4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
A Survey of SOM-Based Active Contour Models for Image Segmentation
Publisher: Springer International Publishing
Date: 2014
From Pixels to Deposits: Porphyry Mineralization With Multispectral Convolutional Neural Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Related Organisations
University Of Nottingham
Location: United Kingdom of Great Britain and Northern Ireland
Birmingham City University
Location: United Kingdom of Great Britain and Northern Ireland
University Of Exeter
Location: United Kingdom of Great Britain and Northern Ireland
University Of Warwick Warwick Medical School
Location: United Kingdom of Great Britain and Northern Ireland
Related Funding Activities
No related grants have been discovered for Mohammed M. Abdelsamea.