Publications

A systematic review of low-rank and local low-rank matrix approximation in big data medical imaging

Published in Springer Nature Neural Comput & Applications, 2025

Medical imaging datasets pose challenges in storage, transmission, and analysis due to their complexity. Low-rank matrix approximation (LORMA) and its variant, LLORMA, offer efficient data representation, with LLORMA gaining preference since 2015 for capturing structural details. This review examines their applications, limitations in similarity measurement, and the potential of semantic segmentation for improvement. We also explore their extension to structured data, the impact of missing values, and propose a hybrid optimization approach to enhance patch selection and overall applicability in medical imaging. Read more

Recommended citation: Hamlomo, S., Atemkeng, M., Brima, Y. et al. A systematic review of low-rank and local low-rank matrix approximation in big data medical imaging. Neural Comput & Applic (2025). https://doi.org/10.1007/s00521-025-11055-2 https://doi.org/10.3390/info15020114

Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction

Published in MDPI Information, 15(2), 114 (2024), 2024

This paper explores self-supervised learning for speech representation, leveraging redundancy reduction techniques to learn robust representations. It adapts the Barlow Twins framework to speech processing and evaluates its effectiveness in various downstream tasks. Read more

Recommended citation: Brima, Y., et al. (2024). Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction. MDPI Information, 15(2), 114. DOI: 10.3390/info15020114 https://doi.org/10.3390/info15020114

Saliency-driven Explainable Deep Learning in Medical Imaging: Bridging Visual Explainability and Statistical Quantitative Analysis

Published in BioData Mining, 17, 18 (2024), 2024

This work explores explainable deep learning techniques in medical imaging, bridging visual explainability with statistical quantitative analysis. It proposes a novel approach that enhances the interpretability of AI models applied to medical diagnostics. Read more

Recommended citation: Brima, Y., Atemkeng, M. (2024). Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysis. BioData Mining, 17, 18. DOI: 10.1186/s13040-024-00370-4 https://doi.org/10.1186/s13040-024-00370-4

Advancing Low-Rank and Local Low-Rank Matrix Approximation in Medical Imaging: A Systematic Literature Review and Future Directions

Published in Accepted at Springer Neural Computing and Applications (NCAA), 2024

This work provides a systematic review of low-rank and local low-rank matrix approximation techniques in medical imaging, offering insights into future research directions and clinical applications. Read more

Recommended citation: Hamlomo, S., Atemkeng, M., Brima, Y., Nunhokee, C., Baxter, J. (2024). Advancing Low-Rank and Local Low-Rank Matrix Approximation in Medical Imaging: A Systematic Literature Review and Future Directions. arXiv:2402.14045. (Accepted at Springer Neural Computing and Applications (NCAA)) https://arxiv.org/abs/2402.14045

Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images

Published in Information, 15(4), 182 (2024), 2024

This paper investigates the interpretability of deep learning models in detecting and diagnosing brain tumors from MRI images. It evaluates various explainability techniques and their effectiveness in clinical applications. Read more

Recommended citation: Nhlapho, W., Atemkeng, M., Brima, Y., et al. (2024). Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images. Information, 15(4), 182. DOI: 10.3390/info15040182 https://doi.org/10.3390/info15040182

Learning Disentangled Audio Representations through Controlled Synthesis

Published in ICLR Tiny Papers Track 2024, 2023

This paper introduces a novel approach to learning disentangled speech representations through controlled synthesis, enabling better interpretability and generalization in speech processing tasks. Read more

Recommended citation: Brima, Y., et al. (2023). Learning Disentangled Audio Representations through Controlled Synthesis. ICLR Tiny Papers Track 2024. https://openreview.net/forum?id=Fn9ORH8PLl

Learning Disentangled Speech Representations

Published in New in Machine Learning Workshop, NeurIPS 2023, 2023

This study investigates methods for learning disentangled representations of speech signals, focusing on separating different speech-related factors such as speaker identity, linguistic content, and emotional state. The work applies novel deep learning approaches to improve representation learning in speech processing. Read more

Recommended citation: Brima, Y., Krumnack, U., Pika, S., & Heidemann, G. (2023). Learning Disentangled Speech Representations. New in Machine Learning Workshop, NeurIPS 2023. https://openreview.net/forum?id=3ox1TfKeRF

Self-Supervised Learning of Speech Representation via Redundancy Reduction

Published in Gesellschaft für Informatik e.V.. pp. 11-19. Doctoral Consortium at KI 2023. Berlin. 45195, 2023

This work aims to investigate a novel self-supervised learning (SSL) method for speech representation that leverages redundancy reduction techniques to learn robust representations capturing speaker characteristics. Our proposed approach builds upon the Barlow Twins framework, introduced in computer vision, and we adapt it to speech processing. The primary objective is to assess the quality of the learned representations through comprehensive evaluations of various downstream tasks, including speaker identification, gender recognition, emotion recognition, and more. Read more

Recommended citation: Brima, Yusuf (2023): Self-Supervised Learning of Speech Representation via Redundancy Reduction. DC@KI2023: Proceedings of Doctoral Consortium at KI 2023. DOI: 10.18420/ki2023-dc-02. Gesellschaft für Informatik e.V.. pp. 11-19. Doctoral Consortium at KI 2023. Berlin. 45195 https://doi.org/10.18420/ki2023-dc-02

A Mathematical Framework for Understanding Recognition Systems

Published in arXiv preprint biorxiv:2023.06.08.544240, 2023

We propose a theoretical framework for biological recognition, integrating category theory, information theory, dynamical systems modeling, and optimization theory. This framework defines individual recognition (IR) and class-level recognition (CR) as functors between stimuli and responses. We identify five conditions for robust IR systems, termed “signature systems,” and model them as attractor states, offering insights into communication and language evolution. Read more

Recommended citation: Brima, Yusuf. "A Mathematical Framework for Understanding Recognition Systems" biorxiv preprint biorxiv:2023.06.08.544240 (2023). https://doi.org/10.1101/2023.06.08.544240

Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification

Published in Dhaka University Journal of Applied Science and Engineering, 6(2), 14–29 (2022), 2022

This paper presents a deep transfer learning approach for multi-class classification of brain MRI images, evaluating its effectiveness in differentiating various neurological conditions. Read more

Recommended citation: Brima, Y., Kamal Tushar, M. H., Kabir, U., Islam, T. (2022). Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification. Dhaka University Journal of Applied Science and Engineering, 6(2), 14–29. DOI: 10.3329/dujase.v6i2.59215 https://doi.org/10.3329/dujase.v6i2.59215

Transfer Learning for the Detection and Diagnosis of Types of Pneumonia including Pneumonia Induced by COVID-19 from Chest X-ray Images

Published in Diagnostics, 11(8), 1480 (2021), 2021

This study explores the use of transfer learning for detecting and diagnosing different types of pneumonia, including COVID-19-induced pneumonia, from chest X-ray images. The work evaluates the performance of various deep learning models in assisting medical professionals with automated diagnostics. Read more

Recommended citation: Brima, Y., Atemkeng, M., Tankio Djiokap, S., Ebiele, J., & Tchakounté, F. (2021). Transfer Learning for the Detection and Diagnosis of Types of Pneumonia including Pneumonia Induced by COVID-19 from Chest X-ray Images. Diagnostics, 11(8), 1480. DOI: 10.3390/diagnostics11081480 https://doi.org/10.3390/diagnostics11081480

Brain MRI Dataset

Published in Figshare/Dataset, 2021

Published a dataset that was curated in collaboration between the Computer Science and Engineering Department, University of Dhaka and the National Institute of Neuroscience, Bangladesh. It comprise 5,285 T1-weighted contrast- enhanced brain MRI images belonging to 38 categories. Read more

Recommended citation: Yusuf Brima, Mossadek Hossain Kamal, Upama Kabir, and Tariqul Islam (2021). "Brain MRI Dataset." Figshare/Dataset. 1(1). https://doi.org/10.6084/m9.figshare.14778750.v2