A training initiative organised by the South African Radio Astronomy Observatory (SARAO)

6th Big Data Africa School

Cape Town, 04 – 10 October 2026
Registration Deadline: 3 May 2026

Theme: Machine / Deep Learning and Novel Quantum Computing Techniques applied to Medical Imaging

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The Big Data Africa School aims to introduce fundamental data science tools & techniques to talented young science and engineering graduates across a range of disciplines, who have an interest to develop their skills and knowledge in working efficiently on extremely large datasets in any research environment.

The 6th Big Data Africa School allows students to work on real-life data sets in the area of healthcare focusing on medical imaging by solving some of the biggest challenges facing the African continent.

Who can apply?

Eligible countries* – South Africa, Botswana, Ghana, Namibia, Kenya, Mauritius, Madagascar, Mozambique, Zambia

  1. Students currently undertaking their 4th year BSc Honours or final year BEng degree, Master of Science / Engineering or PhD degree in Bioinformatics / Computational Biology / Computer Science / Radiography / Diagnostic Ultrasound / Nuclear Medicine Technology / Radiation Therapy / Computer or Biomedical Engineering.
  2. Students in disciplines outside of the above domains are welcome to apply.
  3. Intermediate to advanced programming skills will be advantageous to applicants. Python will be the programming language used at the Big Data Africa School.

Application enquiries can be emailed to bigdataschool@ska.ac.za

Partners and Contributors

The 6th Big Data Africa School is funded by the UK’s International Science Partnerships Fund through the Development in Africa with Radio Astronomy (DARA) project.

View and download the Brochure below

View and download the Digital Booklet below

For more information contact:

Dr Bonita de Swardt
Programme Manager: Strategic Partnerships for Human Capital Development
Email: bonita@sarao.ac.za

Big Data Africa School – Projects

  • Interpreting AI Decisions in Medical Image Classification

  • Disparities in Demographic Performance for AI Medical Imaging Applications

  • Novel Biological Insights using Quantum Computers

  • Medical Image Reconstruction - Developing Data Driven Priors for Magnetic Resonance Imaging (MRI) Using Generative Deep Learning

  • AI / ML for prognostic biomarkers in Idiopathic Pulmonary Fibrosis

Project 1: Self-Explainable AI vs. Post-Hoc Explanations: Interpreting AI Decisions in Medical Image Classification

Convolutional Neural Networks (CNNs) have been widely adopted across various sectors, including healthcare, due to their remarkable ability to solve complex medical imaging tasks, often surpassing human performance. However, despite their impressive potential for medical image classification, CNN-based systems are often considered “black boxes,” as their decision-making processes remain opaque to users. This lack of transparency poses a significant barrier to their adoption in clinical settings, where interpretability is essential.

Project 2: Disparities in Demographic Performance for AI Medical Imaging Applications

Deep learning (DL) models for medical image analysis have achieved strong predictive performance; however, concerns regarding demographic bias and fairness persist, particularly in dermatological applications where skin tone diversity is crucial. While balancing training datasets is often proposed as a mitigation strategy, recent evidence suggests that data composition alone cannot fully explain or resolve demographic performance disparities.

Project 3: Novel Biological Insights using Quantum Computers

Breast cancer is one of the leading causes of death in women worldwide, with 2.3 million diagnosed and 670 000 deaths in 2022 alone. Understanding the interactions amongst tumour cells within the tumour microenvironment may accelerate therapeutic discovery. Novel tools to assist pathologists for diagnosis and prognosis are needed. Harnessing the laws of quantum mechanics, quantum computers offer unparalleled opportunity for discovery through its ability to compute problems far beyond what classical computers can do alone. Thus, this project will allow participants to explore how to combine classical supercompute with quantum compute by building a hybrid workflow for breast cancer classification. Participants will learn quantum machine learning protocols, train a QNN of their choice, evaluate their findings and present their results for recommendations for next experimental steps.

Project 4: Medical Image Reconstruction – Developing Data Driven Priors for Magnetic Resonance Imaging (MRI) Using Generative Deep Learning

Magnetic resonance imaging (MRI) is a crucial medical imaging modality, but its clinical utility is often limited by long acquisition times. Accelerating MRI by undersampling k-space data transforms image reconstruction into an ill-posed inverse problem that requires careful regularization. Traditional approaches rely on hand-crafted priors such as total variation or wavelet sparsity, which may not fully capture the complex structure of anatomical images. Recently, deep learning methods have shown remarkable success in MRI reconstruction, but most require large paired training datasets and lack principled uncertainty quantification.

Project 5: AI / ML for prognostic biomarkers in Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease marked by irreversible scarring. IPF worsens over time, so understanding disease progression is essential for better disease management and ultimately, to improve patient outcomes. In this project students will learn how to extract quantitative features from three-dimensional CT scans, using the extracted quantitative features to build machine learning (ML) models that can serve as robust prognostic biomarkers for IPF progression.

Contacts

Dr Bonita de Swardt

Programme Manager: Strategic Partnerships for Human Capital Development

South African Radio Astronomy Observatory (SARAO)

Liesbeek House
River Park, Glouchester Road,
Mowbray, 7700,
Cape Town, South Africa

Email: bonita@sarao.ac.za

Website: https://www.sarao.ac.za

Dr Celia Cintas

Research Scientist

IBM Research Africa, Nairobi, Kenya

Email: Celia.Cintas@ibm.com

Website: https://research.ibm.com/people/celia-cintas