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portfolio
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publications
Risk Assessment of Computer-Aided Diagnostic Software for Hepatic Resection
Published in IEEE Transactions on Radiation and Plasma Medical Sciences, 2021
This article evaluates the indirect relationship between adopting computer-aided detection or diagnostic (CADe or CADx) systems for hepatic resection and their impact on patient health post-surgery through extensive simulations.
Recommended citation: Akhtar, Y., Dakua, S. P., Abdalla, A., Aboumarzouk, O. M., Ansari, M. Y., Abinahed, J., Elakkad, M. S. M., & Al-Ansari, A. (2021). Risk Assessment of Computer-Aided Diagnostic Software for Hepatic Resection. IEEE Transactions on Radiation and Plasma Medical Sciences.
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Re-routing drugs to blood-brain barrier: A comprehensive analysis of machine learning approaches with fingerprint amalgamation and data balancing
Published in IEEE Access, 2022
This comprehensive analysis explores machine learning approaches for re-routing drugs across the blood-brain barrier, emphasizing fingerprint amalgamation and data balancing techniques.
Recommended citation: Ansari, M. Y., Chandrasekar, V., Singh, A. V., & Dakua, S. P. (2022). Re-routing drugs to blood-brain barrier: A comprehensive analysis of machine learning approaches with fingerprint amalgamation and data balancing. IEEE Access.
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Practical utility of liver segmentation methods in clinical surgeries and interventions
Published in BMC Medical Imaging, 2022
This comprehensive review evaluates various liver segmentation methods and their practical applications in clinical surgeries and interventions, emphasizing their impact on diagnosis, staging, and treatment planning for hepatocellular carcinoma.
Recommended citation: Ansari, M. Y., Abdalla, A., Ansari, M. Y., Ansari, M. I., Malluhi, B., Mohanty, S., Mishra, S., Singh, S. S., Abinahed, J., Al-Ansari, A., Balakrishnan, S., & Dakua, S. P. (2022). Practical utility of liver segmentation methods in clinical surgeries and interventions. BMC Medical Imaging.
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A lightweight neural network with multiscale feature enhancement for liver CT segmentation
Published in Scientific Reports, 2022
This paper introduces Res-PAC-UNet, a novel neural network architecture that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions for efficient and precise liver CT segmentation.
Recommended citation: Ansari, M. Y., Yang, Y., Balakrishnan, S., Abinahed, J., Al-Ansari, A., Warfa, M., Almokdad, O., Barah, A., Omer, A., Singh, A. V., Meher, P. K., Bhadra, J., Halabi, O., Azampour, M. F., Navab, N., Wendler, T., & Dakua, S. P. (2022). A lightweight neural network with multiscale feature enhancement for liver CT segmentation. Scientific Reports.
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Dense-PSP-UNet: A neural network for fast inference liver ultrasound segmentation
Published in Computers in Biology and Medicine, 2023
Dense-PSP-UNet is a neural network architecture designed for rapid and accurate liver ultrasound image segmentation, enhancing diagnostic efficiency.
Recommended citation: Ansari, M. Y., Yang, Y., Meher, P. K., & Dakua, S. P. (2023). Dense-PSP-UNet: A neural network for fast inference liver ultrasound segmentation. Computers in Biology and Medicine.
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MeFood: A large-scale representative benchmark of quotidian foods for the Middle East
Published in IEEE Access, 2023
MeFood introduces a large-scale benchmark dataset representing everyday foods from the Middle East, aimed at advancing food recognition research.
Recommended citation: Ansari, M. Y., & Qaraqe, M. (2023). MeFood: A large-scale representative benchmark of quotidian foods for the Middle East. IEEE Access.
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Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta
Published in IEEE Access, 2023
This study explores the application of machine learning models to predict drug permeability across the placental barrier, offering insights into computational alternatives to animal testing for pregnant populations.
Recommended citation: Chandrasekar, V., Ansari, M. Y., Singh, A. V., Uddin, S., Prabhu, K. S., Dash, S., Al Khodor, S., Terranegra, A., Avella, M., & Dakua, S. P. (2023). Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta. IEEE Access.
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Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade
Published in Artificial Intelligence in Medicine, 2023
This review examines advancements in estimating age and gender from electrocardiogram (ECG) signals over the past decade, highlighting methodologies, challenges, and future directions.
Recommended citation: Ansari, M. Y., Qaraqe, M., Charafeddine, F., Serpedin, E., Righetti, R., & Qaraqe, K. (2023). Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade. Artificial Intelligence in Medicine.
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Efficacy of fusion imaging for immediate post-ablation assessment of malignant liver neoplasms: A systematic review
Published in Cancer Medicine, 2023
This systematic review evaluates the effectiveness of fusion imaging techniques in assessing immediate post-ablation therapeutic responses in malignant liver neoplasms.
Recommended citation: Rai, P., Ansari, M. Y., Warfa, M., Al-Hamar, H., Abinahed, J., Barah, A., Dakua, S. P., & Balakrishnan, S. (2023). Efficacy of fusion imaging for immediate post-ablation assessment of malignant liver neoplasms: A systematic review. Cancer Medicine.
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Neural network-based fast liver ultrasound image segmentation
Published in International Joint Conference on Neural Networks (IJCNN), 2023
This paper presents a neural network model designed for rapid and accurate liver ultrasound image segmentation, enhancing clinical workflow efficiency.
Recommended citation: Ansari, M. Y., Mangalote, I. A. C., Masri, D., & Dakua, S. P. (2023). Neural network-based fast liver ultrasound image segmentation. In 2023 International Joint Conference on Neural Networks (IJCNN).
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Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound
Published in Frontiers in Oncology, 2023
This critical review explores the application of generative adversarial networks (GANs) in elastography ultrasound for breast cancer assessment, discussing current advancements and future prospects.
Recommended citation: Ansari, M. Y., Qaraqe, M., Righetti, R., Serpedin, E., & Qaraqe, K. (2023). Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound. Frontiers in Oncology.
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Towards Developing a Lightweight Neural Network for Liver CT Segmentation
Published in Medical Imaging and Computer-Aided Diagnosis, 2023
This study explores the development of a lightweight neural network tailored for efficient liver CT image segmentation, aiming to enhance diagnostic accuracy and computational efficiency.
Recommended citation: Ansari, M. Y., Mohanty, S., Mathew, S. J., Mishra, S., Singh, S. S., Abinahed, J., & Dakua, S. P. (2023). Towards Developing a Lightweight Neural Network for Liver CT Segmentation. In Medical Imaging and Computer-Aided Diagnosis. Springer, Singapore.
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Advancements in Deep Learning for B-Mode Ultrasound Segmentation: A Comprehensive Review
Published in IEEE Transactions on Emerging Topics in Computational Intelligence, 2024
This survey systematically examines advancements in deep learning techniques for B-Mode ultrasound segmentation, focusing on loss functions, metrics, and neural network architectures.
Recommended citation: Ansari, M. Y., Mangalote, I. A. C., Meher, P. K., Aboumarzouk, O., Al-Ansari, A., Halabi, O., & Dakua, S. P. (2024). Advancements in Deep Learning for B-Mode Ultrasound Segmentation: A Comprehensive Review. IEEE Transactions on Emerging Topics in Computational Intelligence.
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Enhancing ECG-based heart age: impact of acquisition parameters and generalization strategies for varying signal morphologies and corruptions
Published in Frontiers in Cardiovascular Medicine, 2024
This paper evaluates the impact of ECG acquisition parameters and proposes generalization strategies for improving ECG-based heart age estimation under varying signal morphologies and corruptions.
Recommended citation: Ansari, M. Y., Qaraqe, M., Righetti, R., Serpedin, E., & Qaraqe, K. (2024). Enhancing ECG-based heart age: impact of acquisition parameters and generalization strategies for varying signal morphologies and corruptions. Frontiers in Cardiovascular Medicine.
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CoLoSSI: Multi-Robot Task Allocation in Spatially-Distributed and Communication Restricted Environments
Published in IEEE Access, 2024
CoLoSSI introduces a cooperative, load-balancing task allocation framework for multi-robot systems, addressing non-atomic task models and enabling cooperation in communication-restricted environments.
Recommended citation: Ansari, I., Mohammed, A., Ansari, Y., Ansari, M. Y., Razak, S., & Flushing, E. F. (2024). CoLoSSI: Multi-Robot Task Allocation in Spatially-Distributed and Communication Restricted Environments. IEEE Access.
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GeoCrack: A high-resolution dataset for segmentation of fracture edges in geological outcrops
Published in Scientific Data, 2024
GeoCrack is the first large-scale open-source annotated dataset of fracture traces from geological outcrops, enabling deep learning-based fracture segmentation and setting a new standard for natural fracture characterization datasets.
Recommended citation: Yaqoob, M., Ishaq, M., Ansari, M. Y., Konagandla, V. R. S., Tamimi, T. A., Tavani, S., Corradetti, A., & Seers, T. D. (2024). GeoCrack: A high-resolution dataset for segmentation of fracture edges in geological outcrops. Scientific Data.
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Development and Validation of a Class Imbalance-Resilient Cardiac Arrest Prediction Framework Incorporating Multiscale Aggregation, ICA, and Explainability
Published in IEEE Transactions on Biomedical Engineering, 2024
This paper presents a novel framework for cardiac arrest prediction using multiscale feature aggregation and Independent Component Analysis (ICA) to improve explainability, accuracy, and cope with data imbalance.
Recommended citation: Afsa, I., Ansari, M. Y., Paul, S., Halabi, O., Alataresh, E., Shah, J., Hamze, A., Aboumarzouk, O., Al-Ansari, A., & Dakua, S. P. (2024). Development and Validation of a Class Imbalance-Resilient Cardiac Arrest Prediction Framework Incorporating Multiscale Aggregation, ICA, and Explainability. IEEE Transactions on Biomedical Engineering.
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talks
Real-Time Image Segmentation for Enabling Fusion Imaging in Hepatocellular Carcinoma Ablation
Published:
This talk discussed the transformative role of recent advancements in Artificial Intelligence and Deep Learning in medical imaging, specifically for cancer treatment. With a focus on real-time image segmentation, the presentation highlighted how lightweight neural networks enable the fusion of imaging modalities to improve the precision and efficacy of hepatocellular carcinoma ablation procedures. By integrating real-time segmentation into clinical workflows, this talk showcased how deep learning models are being leveraged to identify liver boundaries more accurately, ensure better ablation targeting, and ultimately enhance patient outcomes.
Conference Proceeding talk: Towards Developing a Lightweight Neural Network for Liver CT Segmentation
Published:
This talk focused on the development of Res-PAC-UNet, a cutting-edge neural network tailored for liver CT image segmentation, specifically targeting challenges in the diagnosis and treatment planning of liver diseases like hepatocellular carcinoma (HCC). The network employed Pyramid Atrous Convolutions and a fixed-width residual UNet backbone to achieve high segmentation accuracy with a remarkably low parameter count. This efficient and lightweight architecture demonstrated the potential to enhance clinical workflows by providing precise and scalable solutions for liver CT segmentation, paving the way for improved patient outcomes in liver disease management.
Lightweight Deep Neural Network Framework for Liver CT Segmentation
Published:
This presentation explored the significant advancements in AI-driven medical imaging technologies, emphasizing the development of lightweight deep learning frameworks for liver CT segmentation. Highlighting the growing need for computationally efficient solutions in cancer diagnostics and treatment planning, this talk delved into how lightweight neural networks are revolutionizing the segmentation process. These networks not only reduce computational overhead but also ensure robust segmentation performance, paving the way for improved diagnosis, treatment, and follow-up care for liver-related cancers.
Conference Proceeding talk: Neural Network-Based Fast Liver Ultrasound Image Segmentation
Published:
This talk explored the development of a novel neural network designed specifically for real-time liver ultrasound (US) image segmentation. Emphasizing the challenges posed by noisy and low-contrast ultrasound images, the presentation highlighted how the new network leverages advanced architecture, including Pyramid Scene Parsing, to deliver accurate and efficient segmentation in real-time. By addressing the clinical need for rapid and precise liver segmentation, this talk demonstrated the potential of the network to enhance diagnostic workflows and improve decision-making in liver disease management.
teaching
15-112: Fundamentals of Programming (Course Assistant)
Undergraduate course, Carnegie Mellon University, Doha Campus, 2017
Course Assistant for 15-112: Fundamentals of Programming during the following semesters: Fall 2019, Fall 2018, Spring 2018, Fall 2017, and Spring 2017. Responsibilities included guiding students through core programming concepts, providing debugging support, helping with homeworks and project, and grading exams.
62-146: Looking at Making (Course Assistant)
Undergraduate course, Carnegie Mellon University, Doha Campus, 2018
Course Assistant for 62-146: Looking at Making during Spring 2018. Responsibilities included assisting students in the 3D printer lab by helping them convert 3D design files into printer-compatible formats, tuning printing parameters for optimal results, and managing print schedules to ensure all students had access to printing resources. Additional responsibilities involved supporting students during hands-on design and fabrication exercises and providing guidance on project execution.
62-238: Looking at Shapes (Course Assistant)
Undergraduate course, Carnegie Mellon University, Doha Campus, 2018
Course Assistant for 62-238: Looking at Shapes during Spring 2018. Responsibilities included assisting the instructor in preparing course material, guiding students on spatial and geometric concepts, grading assignments and projects, and providing support during class discussions and project reviews.
15-110: Principles of Computing (Course Assistant)
Undergraduate course, Carnegie Mellon University, 2020
Course Assistant for 15-110: Principles of Computing during Spring 2020. Responsibilities included supporting students with fundamental computing concepts, grading assignments, and assisting with debugging programming tasks during lab sessions.
ICT-668: Medical Image Processing (Lab Instructor)
Graduate course, Hamad Bin Khalifa University, 2023
Teaching Assistant/Lab Instructor for Medical Image Processing during Fall 2023, emphasizing practical applications of image processing techniques in medical diagnostics. Responsibilities included creating recitation material on image analysis algorithms and medical imaging techniques, designing and implementing lab exercises for hands-on experience with medical imaging tools, and mentoring students during class projects.
Engr-102: Engineering Computation (Lab Instructor)
Undergraduate course, Texas A&M University, 2024
Teaching Assistant/Lab Instructor for during Fall 2024, focusing on Python-based computational problem solving. Responsibilities included developing and delivering recitation material on Python programming and algorithms, designing lab exercises to enhance coding proficiency, grading all assignments and quizzes, and proctoring exams and quizzes.