Abder-Rahman Ali
The latest research papers by Abder-Rahman Ali, gathered from arXiv, OpenAlex, Semantic Scholar and more. Follow Abder-Rahman Ali on Distill AI and their newest work surfaces at the top of your feed the moment it’s published — never miss a paper.
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- DEVELOPMENT OF AUTOMATIC MEASUREMENT FUNCTION FOR ULTRASOUND-GUIDED ATTENUATION PARAMETER (UGAP) AND SHEAR WAVE ELASTOGRAPHYTakuma Oguri, Naohisa Kamiyama, Michael H. Wang, Dutta Komal et al. · Ultrasound in Medicine & Biology · Jan 1, 2025
- Simple is More: Efficient Liver View Classification in Ultrasound Images Using Minimal Labeled Data and Simple Neural Network ArchitectureAbder-Rahman Ali, Anthony E. Samir · Lecture notes in computer science · Oct 24, 2024
- Prompt-driven Universal Model for View-Agnostic Echocardiography AnalysisSekeun Kim, Hui Ren, Peng Guo, Abder-Rahman Ali et al. · arXiv (Cornell University) · Apr 9, 2024
Echocardiography segmentation for cardiac analysis is time-consuming and resource-intensive due to the variability in image quality and the necessity to process scans from various standard views. While current automated segmentation methods…
- Liver Segmentation in Ultrasound Images Using Self-Supervised Learning with Physics-inspired Augmentation and Global-Local RefinementAbder-Rahman Ali, Peng Guo, Anthony E. Samir · OpenAlex · Jun 5, 2023
Shear Wave Elastography (SWE) is a non-invasive ultrasound method that evaluates changes in liver stiffness, serving as a useful biomarker for liver fibrosis. The proper placement of a region of interest (ROI) on the liver in the B-mode ima…
- Self-Supervised Learning for Accurate Liver View Classification in Ultrasound Images with Minimal Labeled DataAbder-Rahman Ali, Anthony E. Samir, Peng Guo · OpenAlex · Jun 1, 2023
Conventional B-mode "grey scale" medical ultrasound and shear wave elastography (SWE) are widely used for chronic liver disease diagnosis and risk stratification. Liver disease is very common and is clinically and socially important. As a r…
- A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray imagesCosimo Ieracitano, Nadia Mammone, Mario Versaci, Giuseppe Varone et al. · Neurocomputing · Jan 20, 2022
- The Significance of Requirements in Medical Device Software DevelopmentMartin McHugh, Abder-Rahman Ali, Fergal McCaffery · Arrow@dit (Dublin Institute of Technology) · Feb 19, 2021
Software to be used in or as a medical device is subject to user requirements. However, unlike\nunregulated software, medical device software must meet both the user’s requirements and\nthe requirements of the regulatory body of the region …
- The Impact of Fuzzy Requirements on Medical Device Software DevelopmentMartin McHugh, Abder-Rahman Ali, Fergal McCaffery · Arrow@dit (Dublin Institute of Technology) · Feb 17, 2021
Any software development project can experience difficulties with unclear or vague requirements. Unfortunately, this problem can be experience two fold in regulated environments such as the medical device software development industry. In t…
- Towards the early detection of melanoma by automating the measurement of asymmetry, border irregularity, color variegation, and diameter in dermoscopy imagesAbder-Rahman Ali · Stirling Online Research Repository (University of Stirling) · Sep 11, 2020
The incidence of melanoma, the most aggressive form of skin cancer, has increased more than many other cancers in recent years. The aim of this thesis is to develop objective measures and automated methods to evaluate the ABCD (Asymmetry, B…
- A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic ImagesAbder-Rahman Ali, Jingpeng Li, Summrina Kanwal, Guang Yang et al. · Frontiers in Medicine · Jul 7, 2020
Skin lesion border irregularity, which represents the B feature in the ABCD rule, is considered one of the most significant factors in melanoma diagnosis. Since signs that clinicians rely on in melanoma diagnosis involve subjective judgment…
- A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic imagesAbder-Rahman Ali, Jingpeng Li, Guang Yang, Sally J. O’Shea · PeerJ Computer Science · Jun 29, 2020
Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule. In this article we propose an automated approach for skin lesion border irregulari…
- Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic imagesAbder-Rahman Ali, Jingpeng Li, Sally J. O’Shea · PLoS ONE · Jun 16, 2020
Asymmetry, color variegation and diameter are considered strong indicators of malignant melanoma. The subjectivity inherent in the first two features and the fact that 10% of melanomas tend to be missed in the early diagnosis due to having …
- Automating the ABCD Rule for Melanoma Detection: A SurveyAbder-Rahman Ali, Jingpeng Li, Guang Yang · IEEE Access · Jan 1, 2020
The ABCD rule is a simple framework that physicians, novice dermatologists and non-physicians can use to learn about the features of melanoma in its early curable stage, enhancing thereby the early detection of melanoma. Since the interpret…
- Skin cancer detection based on deep learning and entropy to detect outlier samplesAndré G. C. Pacheco, Abder-Rahman Ali, Thomas Trappenberg · arXiv (Cornell University) · Sep 10, 2019
We describe our methods that achieved the 3rd and 4th places in tasks 1 and 2, respectively, at ISIC challenge 2019. The goal of this challenge is to provide the diagnostic for skin cancer using images and meta-data. There are nine classes …
- A Deep Learning Based Approach to Skin Lesion Border Extraction With a Novel Edge Detector in Dermoscopy ImagesAbder-Rahman Ali, Jingpeng Li, Sally J. O’Shea, Guang Yang et al. · OpenAlex · Jul 1, 2019
Lesion border detection is considered a crucial step in diagnosing skin cancer. However, performing such a task automatically is challenging due to the low contrast between the surrounding skin and lesion, ambiguous lesion borders, and the …
- Supervised Versus Unsupervised Deep Learning Based Methods for Skin Lesion Segmentation in Dermoscopy ImagesAbder-Rahman Ali, Jingpeng Li, Thomas Trappenberg · Lecture notes in computer science · Jan 1, 2019
- Fuzzy C-Means based on Minkowski distance for liver CT image segmentationAbder-Rahman Ali, Micael S. Couceiro, Aboul Ella Hassanien, D. Jude Hemanth · Intelligent Decision Technologies · Jul 12, 2016
This paper presents a Fuzzy C-Means based image segmentation approach that benefits from the Minkowski distance as the dissimilarity measure, denoted as FCM-M, instead of the traditional Euclidean distance, herein identified as FCM-E. The p…
- Smoothed shock filtered defuzzification with Zernike moments for liver tumor extraction in MR imagesAntoine Vacavant, Abder-Rahman Ali, Manuel Grand-Brochier, Adélaïde Albouy-Kissi et al. · OpenAlex · Nov 1, 2015
In this paper, we propose to segment liver tumor within ROI (Regions Of Interest) of MR (Magnetic Resonance) images by combining the smoothed shock noise reduction filter, that we introduced in a previous work, and a fuzzy approach. Besides…
- Liver Lesion Extraction With Fuzzy Thresholding In Contrast Enhanced Ultrasound ImagesAbder-Rahman Ali, Adélaïde Albouy-Kissi, Manuel Grand-Brochier, Ladan-Marcus, Viviane et al. · Zenodo (CERN European Organization for Nuclear Research) · Aug 4, 2015
In this paper, we present a new segmentation approach for focal liver lesions in contrast enhanced ultrasound imaging. This approach, based on a two-cluster Fuzzy C-Means methodology, considers type-II fuzzy sets to handle uncertainty due t…
- Particle Swarm Optimization Based Fast Fuzzy C-Means Clustering for Liver CT SegmentationAbder-Rahman Ali, Micael S. Couceiro, Ahmed M. Anter, Aboul Ella Hassanien · Intelligent systems reference library · Jul 18, 2015
- A Fuzzy Approach To Liver Tumor Segmentation With Zernike MomentsAbder-Rahman Ali, Antoine Vacavant, Manuel Grand-Brochier, Adélaïde Albouy-Kissi et al. · Zenodo (CERN European Organization for Nuclear Research) · Jul 3, 2015
In this paper, we present a new segmentation approach for liver lesions in regions of interest within MRI (Magnetic Resonance Imaging). This approach, based on a two-cluster Fuzzy CMeans methodology, considers the parameter variable compact…
- A Novel Evolutionary Swarm Fuzzy Clustering Approach for Hyperspectral ImageryPedram Ghamisi, Abder-Rahman Ali, Micael S. Couceiro, Jón Atli Benediktsson · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · Mar 19, 2015
In land cover assessment, classes often gradually change from one to another. Therefore, it is difficult to allocate sharp boundaries between different classes of interest. To overcome this issue and model such conditions, fuzzy techniques …
- Melanoma detection using fuzzy C-means clustering coupled with mathematical morphologyAbder-Rahman Ali, Micael S. Couceiro, Aboul Ella Hassenian · OpenAlex · Dec 1, 2014
This paper proposes a Fuzzy C-Means (FCM) based approach designed for melanoma diagnosis. The methodology comprises the traditional data processing architecture, including pre-processing (contrast stretching), main processing (FCM) and post…
- PSilhOuette: Towards an Optimal Number of Clusters Using a Nested Particle Swarm Approach for Liver CT Image SegmentationAbder-Rahman Ali, Micael S. Couceiro, Aboul Ella Hassenian · Communications in computer and information science · Jan 1, 2014
- Fuzzy C-Means Based Liver CT Image Segmentation with Optimum Number of ClustersAbder-Rahman Ali, Micael S. Couceiro, Aboul Ella Hassanien, Mohamed F. Tolba et al. · Advances in intelligent systems and computing · Jan 1, 2014