Biomedical Imaging and AI with MATLAB: Hands-On Workshop

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Biomedical imaging is at the heart of many advances in healthcare, from improving diagnostics to enabling intelligent medical technologies. By combining imaging with Artificial Intelligence (AI), researchers can uncover patterns in complex datasets, automate image analysis, and develop powerful predictive models that accelerate research and clinical decision-making. In this hands-on workshop, you will learn how to build AI applications for biomedical imaging using MATLAB. We will explore the complete AI workflow, including image preprocessing, labeling, machine learning, deep learning, and deployment.

Highlights include -

• Analyze, visualize, and preprocess biomedical imaging datasets (e.g., DICOM and multidimensional images)
• Annotate and label image data for ML and DL workflows
• Develop AI models for image classification and segmentation
• Optimize and explain Deep Learning models
• Automatically generate code for cloud, desktop, and edge deployment of AI models
• Utilize MATLAB Copilot and Generative AI workflows to accelerate algorithm development and research

Presenter's Bio:

Ram Krishnamurthy is a Principal Education Application Engineer at MathWorks, where he leads strategic collaborations with universities to strengthen the bridge between academia and industry. He partners with faculty, researchers, and institutional leaders to advance computational thinking, modernize engineering curricula, and prepare students with the analytical and data-driven skills needed to succeed in an evolving technology landscape. His expertise lies in applying Artificial Intelligence and Machine Learning to image and signal processing workflows, driving innovation across both research and real-world engineering domains. Ram holds a Master’s degree in Electrical Engineering from the University of Colorado Boulder.

*This training is presented by the Yale Center for Research Computing and MathWorks.