I am a Biomedical AI Researcher with experience in medical image analysis, multimodal clinical AI, explainable AI, and robust representation learning. My work focuses on building clinically meaningful machine learning systems for imaging and structured health data, with attention to generalization, interpretability, and clinical translation. [[Curriculum Vitae](Curriculum Vitae)]

Ph.D. in Cognitive Science at Osnabrück University, advised by Prof. Gunther Heidemann and Prof. Simone Pika.

MSc. in Mathematical Sciences at The African Institute of Mathematical Sciences (AIMS), advised by Prof. Marcellin Atemkeng.

MSc. student in the Computer Science and Engineering Department at University of Dhaka, advised by Prof. Mosaddek Hossain Kamal.

BSc. in Computer Science from the University of Makeni.

Sankey diagram of research funding sources, disease areas, and institutional affiliations for AI in Neglected Tropical Diseases (NTDs). — Sankey diagram of research funding sources, disease areas, and institutional affiliations AI in Neglected Tropical Diseases (NTDs) between 2015 to 2025.

Kullback–Leibler divergence between two probability distributions. — A web-based DICOM viewer enabling medical image visualization and basic manipulations, such as contrast adjustment, export to png and video (for multiple uploaded files).For more details, visit the project repository: DICOMViewer

Jensen–Shannon divergence between two probability distributions. — A web-based DICOM viewer enabling medical image visualization and basic manipulations, such as contrast adjustment, export to png and video (for multiple uploaded files).For more details, visit the project repository: DICOMViewer

Time-domain, frequency-domain and time-frequency representations of a sine waveform across different frequencies sampled at 16KHz for 1 seconds. The top row is the original signal and the bottom row is the reconstructed version for the disentangled Variational Auto Encoder.

Time-domain, time-frequency and frequency-domain representations of a sawtooth waveform across different frequencies sampled at 16KHz for 2 seconds. — Time-domain, time-frequency and frequency-domain representations of a sawtooth waveform across different frequencies sampled at 16KHz for 2 seconds each.

Triplet-based contrastive representation learning of MNIST validation set. — Visualizing the convergce of clusters of classes in the MNIST validation set using both Triplet (Left) and Supervised Contrastive Representation Learning

128-dimensional latent representation of modified Barlow Twins. — Visualizing the convergence of latent representations over training iterations for modified Barlow Twins model: decorrelated audio representations (left) and original small training data (right). The 128-dimensional latent space reveals the progression of learned features and their alignment throughout the training process.

Explainable AI in Medical Image Analysis using Visual Saliency Maps. Feature attribution was done with a trained InceptionResNetv2 model.

Workshop Poster Presentation — Learnt 2D and 3D principal component latent representations of both human and non-human primates.

Recent News

May 26, 2026; Our Tabular Foundation Model manuscript titled “Few-shot Learning with Cross-country Generalization of Tabular Machine Learning and Foundation Models for Child- hood Anemia Prediction under Distribution Shift” submitted to Nature Machine Intelligence.
April 20, 2026; Our LLM-Guided XAI for Medical Imagge Diagnostics manuscript titled “Bridging Visual Saliency and Large Language Models for Self-explainable Deep Learning in Medical Imaging” submitted to BioData Mining.
May 19, 2026; Our systematic review title “Transformers for 3D Medical Image Analysis: A Systematic Review of Architectural Innovations, Performance, and Clinical Applications,” got accepted at Artificial Intelligence Review.
April 16, 2026; Our AI in NTD Consortium manuscript titled “Artificial Intelligence in Neglected Tropical Diseases: Current Applications, Challenges, and Opportunities” submitted to npj Digital Medicine.
December 24, 2025; Completed “ AI Applications in Healthcare Specialization by Stanford Online” with a focus on AI techniques and their applications in biomedicine. Certificate available at here.
December 24, 2025; Completed “ Evaluations of AI Applications in Healthcare by Stanford Online” with a focus on AI techniques and their applications in medical diagnosis. Certificate available at here.
December 18, 2025; Completed “Fundamentals of Machine Learning for Healthcare by Stanford Online” with a focus on machine learning techniques and their applications in healthcare. Certificate available at here.
December 17, 2025; Completed “Introduction to Clinical Data by Stanford Online” with a focus on clinical data types, sources, and applications in healthcare. Certificate available at here.
December 06, 2025; Completed “Introduction to Healthcare by Stanford Online” with a focus on healthcare systems, quality improvement, and patient safety. Certificate available at here.
November 19, 2025; PhD Thesis Published in the collection: FB08 - E-Dissertations: “Disentangled Representation Learning in Speech and Vocalization “ available at Osnabrück University Repositorium osnaDocs.
August 28, 2025; Gave a talk on Artificial Intelligence and Its Role in Shaping Our Future at the University of Makeni Computer Science Alumni Forum.
June 27, 2025; Successfully defended my PhD thesis at the Institute of Cognitive Science, Osnabrück University.
May 01, 2025; Started a Biomedical Research Fellow at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) in St. Augustin, Germany, focusing on “Federated Learning for Lung Cancer Prognosis” under the supervision of Prof. Dr. rer. nat. Holger Fröhlich.
March 30, 2025; Made a first commit to a Pytorch-based XAI for Dementia using 3D CNN and NIFTI Data
March 04, 2025; “A systematic review of low-rank and local low-rank matrix approximation in big data medical imaging” published for publication in Springer Nature Neural Computing and Applications.
February 19, 2025; Talk on “Multimodal Federated Learning for Robust Lung Cancer Prognosis” at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI).
January 31, 2025; Completed the MUST Deep Learning Bootcamp, North-West University, South Africa MUST Deep Learning Bootcamp 2025.
January 22, 2025; PhD Dissertation Submitted and approved for examination and graduation: “Disentangled Representation Learning in Speech and Vocalization”.
January 20, 2025; The Association of Commonwealth Universities Case Study on “Advancing artificial intelligence for healthcare and developing human capital in low-resource settings” published in ACU Research.
January 11, 2025; “Learning Disentangled Speech Representations” preprint published in arXiv:2311.03389v4.
December 16, 2024; Talk on “Trustworthy Healthcare AI for Mental Health Risk Prediction” at the Neurobiology Research Unit,Copenhagen University Hospital
December 10, 2024; Talk on “Assessing Explainability in Deep Learning for Medical Image Analysis” at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI).
November 11, 2024; Launched SyncSpeech Datasets: A large-scale collection of synthetic speech datasets for speech representation learning. Available at SyncSpeech Datasets.
July 10, 2024; Talk on “Assessing fusarium oxysporum disease severity in cotton using unmanned aerial system images and a hybrid domain adaptation deep learning time series model” at the Leibniz Institute for Agricultural Engineering and Bioeconomy
June 22, 2024; “Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysis” published in BioData Mining 17, 18.
March 27, 2024; “Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images” published in MDPI Information 2024, 15(4), 182.
March 21, 2024; “Got accepted to attend in-person the Deep Learning + Reinforcement Learning (DLRL) Summer School 2024 by CIFAR and the Vector Institute in Toronto, Canada.”.
March 13, 2024; Talk on “Causal Representation Learning” at the Computer Vision Colloquium, Osnabrueck University.
February 16, 2024; “Learning Disentangled Audio Representations through Controlled Synthesis” accepted for oral presentation at ICLR Tiny Papers 2024.
February 15, 2024; “Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction” accepted for oral presentation at MDPI Information 2024, 15(2), 114.
February 12, 2024; “Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Classification from MRI Images” submitted to MDPI Information Special Issue: Deep Learning in Medical Image Analysis: Foundations, Techniques, and Applications.
December 10, 2023; Attended NeurIPS 2023 in New Orleans, USA where Presented research poster on disentangled speech representation learning via self-supervision. Sharing novel techniques for learning interpretable and robust speech representations.
November 1, 2023; “Learning Disentangled Speech Representations” accepted for poster presentation at New in ML, NeurIPS 2023.
October 17, 2023; Co-organizing with Ulf Krumnack and Lukas Niehaus a Seminar: Deep Representation Learning (Winter 2023/24) at Osnabrück University.
September 23, 2023; “Self-Supervised Learning of Speech Representation via Redundancy Reduction” extended abstract published at Gesellschaft für Informatik e.V..
August 1, 2023; “Visual Interpretable and Explainable Deep Learning Models for Brain Tumor MRI and COVID-19 Chest X-ray Images” published in arXiv preprint arXiv:2208.00953.
July 26, 2023; “Self-Supervised Learning of Speech Representation via Redundancy Reduction” accepted for oral presentation at KI 2023 – 46th German Conference on Artificial Intelligence, Berlin, Germany.
June 13, 2023; submitted a manuscript to PLOS One A Mathematical Framework for Understanding Recognition Systems.
September 16, 2022; Launched the Graduate Assistance Initiative Network (GAIN) globally with 130+ attendees.
September 11-16, 2022; Attended Mediterranean Machine Learning school, Università Milano Bicocca, Milan, Italy.
August 6-7, 2022; Co-organized with Viktoria Zemliak the “Bridging the Gap between Biological and Artificial Neural Networks” at the (Research Training Group (RTG) in Computational Cognition).
July 29-August 6, 2022; Attended and presented a poster at the Bridging the technological gap – spreading technological innovations in the study of the human and non-human mind at the German Primate Center in Göttingen, Germany.
July 11-29, 2022; Participated in the Neuromatch Academy: Deep Learning intensive hands-on training.
August 16, 2021; “Transfer Learning for the Detection and Diagnosis of Types of Pneumonia Including Pneumonia Induced by the COVID-19 from Chest X-Ray Images” published in MDPI Special Issue on Machine Learning Applications for COVID-19 and Its Complications: Screening, Diagnosis, Treatment, and Prognosis.
June 15, 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 comprises 5,285 T1-weighted contrast-enhanced brain MRI images belonging to 38 categories. Available here Brain MRI Dataset.

Yusuf Brima, Ph.D.

Recent News