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publications
Paper Title Number 4
Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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Intelligent malaria detection and species classification: A case of Rwanda
Published in Proceedings of Ninth International Congress on Information and Communication Technology, 2024
Recommended citation: Bogale, Y., Mukamakuza, C.P., Tuyishimire, E. (2024). Intelligent Malaria Detection and Species Classification: A Case of Rwanda. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Ninth International Congress on Information and Communication Technology. ICICT 2024 2024. Lecture Notes in Networks and Systems, vol 1002. Springer, Singapore. https://doi.org/10.1007/978-981-97-3299-9_41
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A Comparative Analysis of Deep Learning Models for Malaria Plasmodium Classification
Published in IEEE International Conference on Electronics, Circuits and Systems (ICECS), 2024
Recommended citation: C. P. Mukamakuza, A. D. Nishimwe Karasira, E. M. Akpo, Y. A. Bogale, P. Fasouli and M. Salem, "A Comparative Analysis of Deep Learning Models for Malaria Plasmodium Classification," 2024 31st IEEE International Conference on Electronics, Circuits and Systems (ICECS), Nancy, France, 2024, pp. 1-4, doi: 10.1109/ICECS61496.2024.10848723.
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Prediction of PM2. 5 concentration based on ARIMA, LSTM, and TCN models in Kigali, Rwanda
Published in Climate Change AI workshop at NeurIPS, 2024
Leveraging data from the Rwanda Environmental Management Authority (REMA) to forecast PM2.5 levels in Kigali using three machine learning models which analyze daily average PM2.5 levels, aiming to support air quality interventions and improve public health outcomes in Rwanda
Recommended citation: Bogale, Y.A., Kigali, R., Arinloye, C. and Muragijemariya, J., Prediction of PM2. 5 concentration based on ARIMA, LSTM, and TCN models in Kigali, Rwanda.
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Machine Learning–Enhanced Foundation Model Classification of Rhabdoid Features in Clear Cell Renal Cell Carcinoma
Published in Laboratory Investigation, 2025
Developed a foundation model–based neural network classifier achieving 98% accuracy in identifying rhabdoid features in clear cell renal cell carcinoma, improving reproducibility in computational pathology.
Recommended citation: Collins, K., DeMeo, D., Bogale, Y., Sutaria, A. M., Feldman, M., Shiradkar, R., & Sangoi, A. (2026). Machine Learning–Enhanced Foundation Model Classification of Rhabdoid Features in Clear Cell Renal Cell Carcinoma. Laboratory Investigation, 106(3).
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talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
A Microenvironment-Aware Deep Learning Framework Leveraging Gland-Stroma Morphological Signatures for Prostate Cancer Prognostic Risk Stratification
Published:
Presented at the Luddy Ph.D. Research Symposium at Indiana University Indianapolis. This presentation introduced a microenvironment-aware deep learning framework that leverages gland-stroma morphological signatures for prostate cancer prognostic risk stratification. The talk highlighted the integration of computational pathology and deep learning techniques to improve prognostic assessment and support precision oncology research.
teaching
Teaching Assistant
Graduate course, Indiana University Indianapolis, Biomedical Engineering and Informatics Department, 2026
Served as a Teaching Assistant for a graduate-level BIOMEDICAL IMAGE PROCESSING (BMEG-511) course in the Biomedical Engineering and Informatics Department at Indiana University. Responsibilities included preparing course and lab materials, leading weekly hands-on lab sessions, Preparing bi-weekly quizzes, graded assignments and reports, and held office hours to provide academic support and mentoring.