Mohammed Yusuf Ansari

Mohammed Yusuf Ansari

PhD Candidate in Computer Engineering

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

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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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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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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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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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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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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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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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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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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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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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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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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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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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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Conference Papers

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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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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