This talk consists of two videos:
1.1 What is AI, terms, basic definitions, examples - Dr Richard Sidebottom
Introduction to the concepts, terms, opportunities and pitfalls relating to development and use of AI in medical imaging.
1.2 Developing a Deep Learning Model: Simple Example - Dr Daniel Eiroa
This webinar explores how to develop a deep learning model for medical image classification through a simple example. We cover the steps involved in developing a model, such as data preprocessing, model selection, and training. We provide a step-by-step guide on developing a simple deep learning model using Python, TensorFlow, and Keras. Attendees will learn how deep learning can be used in the medical field and how to develop a deep learning model for medical image classification.
1 CPD credit
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Dr Richard SidebottomRichard is a consultant radiologist specialised in breast imaging. His NHS clinical practice is at Cheltenham. He holds a research radiology post at The Royal Marsden Hospital as part of the AI imaging hub where his current research work includes dataset extraction and curation from routinely collected patient records. Richard has an MSc in biomedical engineering and is interested in the future development of imaging technologies. He has been a paid consultant to DeepMind health / Google health since 2017 has where he has contributed to a publication in Nature in 2020 and a successful NHSx AI fund application awarded in 2021. He continues to be involved in their work in mammography analysis and application. Richard is a member of the CRUK OPTIMAM project advisory board.
Dr Daniel EiroaAttending Physician at the Diagnostic Imaging Department of Vall d'Hebron University Hospital. Author of more than 50 communications in congresses, scientific articles and book chapters. He regularly delivers training sessions and lectures on AI and Medical Data Science. Medical Advisor for Medical AI Companies. Reviewer of Radiología and European Radiology and member of the Publications Commission of the Spanish Society of Medical Radiology.