Yusuf Brima, Ph.D.

Yusuf Brima, Ph.D.

Biomedical AI Researcher

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Tech Stack for Deep Learning Explanability Medical Imaging

2 minute read

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Author: Yusuf Brima Read more

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K-Nearest Neighbors from First Principles

5 minute read

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Author: Yusuf Brima Read more

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K-Means Clustering from First Principles

10 minute read

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Author: Yusuf Brima Read more

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Seminar on Representation Learning

2 minute read

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Author: Yusuf Brima Read more

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Dynamical Systems Modelling Part II: Applications

less than 1 minute read

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Authors: Denis Mulumba and Yusuf Brima Read more

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Understanding Sound from First Principles: Part 2

2 minute read

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Author: Yusuf Brima Read more

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Understanding Sound from First Principles

9 minute read

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Author: Yusuf Brima Read more

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Dynamical Systems Modelling Part I: Fundamentals

27 minute read

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Authors: Denis Mulumba and Yusuf Brima Read more

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Earthlings

2 minute read

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Author: Yusuf Brima Read more

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Climatology Analysis Over the Eastern African Region

10 minute read

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Authors: Joy Pamela Kina Kariuki, Origene Tuyishimire and Yusuf Brima Read more

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Role of Digital Learning on Student of Tertiary Institutions in Sierra Leone Sylvanus Koroma Campus

31 minute read

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Author: Yusuf Brima Read more

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Applications and services of the Internet

34 minute read

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Author: Yusuf Brima Read more

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Brief History of the Internet

12 minute read

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Author: Yusuf Brima Read more

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mentorship

Guides to Securing Scholarships Overseas

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Objectives

The democratization of the Internet has opened vistas of opportunities for young people coming from underprivileged backgrounds to gain scholarships for further studies. This massive outpour of information has also saturated facts with fiction about what it takes to gain a prestigious scholarship program amid the ever-growing competitiveness among candidates for a few slot of opportunities. Thus, our aim in this webinar, the first of its kind (at least to our knowledge for the West African context), is to distill the essence of a scholarship, the does and don't in one's quest to get a scholarship and a general thinking paradigm to have to become a successful scholar. The paradigm in question is problem-solving and leadership-oriented ways of thinking because to be successful, one has to be so in the selfless service to humankind. </div> </div> Read more

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Advice to new Queen Elizabeth Commonwealth Scholars at Dhaka University

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My experience was great personally and professionally. As I am very passionate about traveling and meeting people from different cultures, I learned a lot about the country and the people of Bangladesh. I still maintain ties with some friends from here. Read more

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Sampe documents and general instructions for scholarship application

Published:

As mentioned in my previous post, scholarships are offered to candidiate base on specific eligibility criteria which could range from merit (whatever its definition might be) to diversity and inclusion of underrepresented groups. However, the common denominator is, these candidates need financial assistance for professional development.

One key advice which you should bear in mind in your hunt for scholarship opportunities is to avoid plagiarism. I know, we all at one point in time have to stand on the shoulders of giants in order to grow but give credit to whom it is due. Intellectual honesty is very important. You may wonder, why am I taking a tangent and being stoic on this subject?! Simple. Primarily because it is very crucial not only in getting a scholarship but to toward your profession success (or failure) altogether. Pay attention to this truism. You are going to get suggestively tempted to not be original but doing so could be at your detriment. So avoid using other people's motivation letters, study plan or similar documents as your own. Be yourself. Tell your own unique story. That will help you stand out.

Having got that out of the way, here are a few sample documents and templates to help inspire you develop your own application portfolio. This is not exhaustive, neither it is the best there exist. However, it could be a great first step in developing an intuition of how these documents are developed.

Motivation Letter Template
Study Plan Sample
Research Proposal Sample (disclaimer: it's Computer Science-based but the ideas and structure should translate easily to other STEM-based disciplines maybe even beyond)

This page is a work-in-progress and will continue to be expanded upon to aid more smooth application process for potential candidates. Read more

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people

Origene Tuyishimire

Published:

Origene Tuyishimire is an alumnus of the African Institute of Mathematical Sciences (AIMS), Rwanda. Read more

portfolio

publications

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

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

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

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

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

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

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, Krumnack U, Pika S, Heidemann G. (2023). Learning Disentangled Audio Representations through Controlled Synthesis. ICLR Tiny Papers Track 2024. https://openreview.net/forum?id=Fn9ORH8PLl

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

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

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

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

Published in Neural Computing and 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/10.1007/s00521-025-11055-2

An unsupervised XAI framework for dementia detection with context enrichment

Published in Scientific reports, 15, 1 (2025), 2025

This work introduces a framework that evaluates XAI methods by integrating neuroanatomical morphological features with CNN-generated relevance maps for disease classification. We trained a CNN using brain MRI scans from six cohorts: ADNI, AIBL, DELCODE, DESCRIBE, EDSD, and NIFD (N = 3253), including participants that were cognitively normal, with amnestic mild cognitive impairment, dementia due to Alzheimer’s disease and frontotemporal dementia Read more

Recommended citation: Singh, D., Brima, Y., Levin, F., Becker, M., Hiller, B., Hermann, A., Villar-Munoz, I., Beichert, L., Bernhardt, A., Buerger, K. and Butryn, M., 2025. An unsupervised XAI framework for dementia detection with context enrichment. Scientific reports, 15(1), p.39554 https://doi.org/10.1038/s41598-025-26227-2

An Empirical Study of Machine Learning Robustness and Scalability for Imbalanced Tabular Clinical Data in Emergency and Critical Care

Published in arXiv/preprint, 2025

Machine learning can support high-stakes decision-making in emergency and intensive care settings, but severe class imbalance in clinical data limits model reliability and biases predictions toward majority outcomes. We evaluate six model families, including classical methods (Decision Tree, Random Forest, XGBoost), deep learning approaches (TabNet), and tabular foundation models (TabICL, TabPFN v2.6), on MIMIC-IV-ED and eICU datasets across multiple clinical prediction tasks. Models are assessed using Macro F1-score, robustness to increasing imbalance, and computational efficiency. Results show dataset-dependent performance: TabPFN and TabICL perform strongly on MIMIC-IV-ED, while XGBoost leads on eICU. No single model dominates across all settings, but foundation models provide a favorable efficiency–performance trade-off and are increasingly competitive in imbalanced clinical prediction scenarios. Read more

Recommended citation: Brima Y, Atemkeng M. Robustness and Scalability Of Machine Learning for Imbalanced Clinical Data in Emergency and Critical Care. arXiv preprint arXiv:2512.21602. 2025 Dec 25. https://arxiv.org/abs/2512.21602

Artificial intelligence in neglected tropical diseases: current applications, challenges, and opportunities

Published in arXiv/preprint, 2026

Neglected tropical diseases (NTDs) affect over one billion people globally and disproportionately impact low-resource tropical and subtropical regions. While artificial intelligence (AI) offers significant opportunities to strengthen NTD control, the scope, maturity, and translational readiness of existing applications remain unclear. We conducted a systematic review of AI methods applied to NTD-related problems using Scopus and Web of Science, following PRISMA guidelines. Across 289 studies, AI applications were primarily concentrated in diagnosis, surveillance, and prognosis, with most relying on supervised learning from imaging, clinical, or epidemiological data. However, external validation, calibration, and prospective evaluation were limited, and research was geographically concentrated in a small number of countries. Overall, despite rapid growth in the field, substantial gaps remain in methodological rigor, equity, and real-world deployment readiness, highlighting the need for more robust, transparent, and context-aware AI research in NTDs. Read more

Recommended citation: Brima Y, Atemkeng M, Ngueajio MK, Ngueabou Y, Nguembang Fadja A, Bonginkosi N, et al. Artificial intelligence in neglected tropical diseases: current applications, challenges, and opportunities. NPJ Digit Med. 2026. Submitted for publication. https://arxiv.org/abs/2604.00123

Bridging visual saliency and large language models for explainable deep learning in medical imaging

Published in arXiv/preprint, 2026

The lack of interpretability in deep learning remains a major barrier to clinical adoption in medical imaging. We propose a multimodal explainability framework for brain tumor classification that combines CNN-based prediction, saliency-based localization (Grad-CAM, Grad-CAM++, ScoreCAM), neuroanatomical mapping via the Harvard-Oxford atlas, and large language models to generate radiology-style diagnostic reports. Evaluated on 4,834 contrast-enhanced T1-weighted MRI scans across three tumor classes, the framework integrates classification, segmentation, and structured report generation into a unified pipeline. InceptionResNetV2 achieved the best classification performance, while Grad-CAM++ produced the most accurate localization. Among LLMs, Grok3 and LLaMA showed complementary strengths in coherence and readability. The results demonstrate that coupling visual attribution with anatomical grounding and language generation improves interpretability and supports more clinically meaningful AI explanations. Read more

Recommended citation: Nguezet PV, Fute ET, Brima Y, Azanguezet BM, Atemkeng M. Bridging visual saliency and large language models for explainable deep learning in medical imaging. arXiv preprint arXiv:2605.06197. 2026 May 7. https://arxiv.org/abs/2605.06197

Few-shot Cross-country Generalization of Tabular Machine Learning and Foundation Models for Childhood Anemia Prediction under Distribution Shift

Published in arXiv/preprint, 2026

Childhood anemia affects ~40% of children aged 6–59 months globally and is driven by heterogeneous, context-dependent factors that challenge model generalization. We evaluate a transformer-based tabular foundation model (TabPFN v2.6) against classical methods (Logistic Regression, XGBoost, LightGBM) using DHS data from 16 countries (n=68,856) under cross-country and data-scarce settings. Across LOCO, reverse-LOCO, and few-shot evaluations, TabPFN shows superior performance in low-data regimes, with improved calibration (Brier: 0.042; ECE: 0.203) and strong discrimination. While full-data performance differences are modest, results indicate that population heterogeneity dominates predictive performance, and foundation models offer advantages for robust, low-resource global health prediction. Read more

Recommended citation: Brima Y, Atemkeng M, Kallon LH, Niyukuri D, Vacavant A, Saidu S, Chen DG. Few-shot Cross-country Generalization of Tabular Machine Learning and Foundation Models for Childhood Anemia Prediction under Distribution Shift. arXiv preprint arXiv:2605.26589. 2026 May 26. https://arxiv.org/abs/2605.26589

talks

Supervised Contrastive Deep Learning for Individual Recognition in Chimpanzees in the Wild

Published:

Poster title: “Supervised Contrastive Deep Learning for Individual Recognition in Chimpanzees in the Wild “ This talked peered into the study of the origin of human language as a communicative tool. It narrowed down on Individual Recogntition (IR) – the ability of an individual (a receiver) to uniquely identify another individual (a signaler) based on signature cues, a ubiquitous practice of animals that live in fission-fusion systems. IR is crucial to startification of groups, formation of trust and behaviour direction towards others. This talk connect the problem of IR from the perpective of a receiver as an computational problem modelled using deep learning. Read more

teaching

Principles of Programming Languages

Undergraduate courses, University of Makeni, Department of Computer Science, 2017

Course objectives: 

  • To understand and describe syntax and semantics of programming languages
  • To understand data, data types, and basic statements 
  • To understand call-return architecture and ways of implementing them
  • To understand object-orientation, concurrency, and event handling in programming languages
  • To develop programs in non-procedural programming paradigms
Read more

Data Mining and Warehousing

Undergraduate courses, University of Makeni, Department of Computer Science, 2017

Unit I

Data mining – Introduction – Information and production factor – Data mining vs query tools– Data mining in marketing – Self learning computer systems – concept learning – Data mining and the data warehouse. Read more

Server-side programming with PHP

Undergraduate courses, University of Makeni, Department of Computer Science, 2017

Unit-1

PHP Basics: introduction, server installation, syntax, echo/print statement,datatypes,strings,constants,Datatypes,Strings,operators,control statements, loops, php functions, php functions with arguments, php arrays Read more

Relational Database Management Systems

Undergraduate courses, University of Makeni, Department of Computer Science, 2017

Unit-1: Introduction To Database Systems

Database Approach,Actors,DBMS Approach, File System, Database Applications Read more

PRINCIPLES OF PROGRAMMING LOGIC AND DESIGN

Undergraduate courses, Limkokwing University of Creative Technology, Faculty of Information and Communication Technology, 2018

Course objectives: 

  • To expose the basic concepts of system - design, implementation, and management
  • To provide an overview on computerized application program, terminology, conventions, and procedures
  • To developing structured program logic
Read more

Deep Representation Learning Seminar

Graduate/Undergraduate Seminar, University of Osnabrück, Institute of Cognitive Science, 2023

Course Overview

This seminar focuses on Deep Representation Learning, exploring how deep neural networks acquire hierarchical and versatile representations during training. The course combines theoretical insights with practical applications, guiding students through the process of analyzing, evaluating, and improving representations in deep learning models. Participants will reproduce and adapt research papers, write scientific reports, and present their findings. Read more