MIDeL/about.html at main · Mayo-Radiology-Informatics-Lab/MIDeL · GitHub

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<!DOCTYPE html>
<html lang="en">
    <head>
        <title>MIDeL: About</title>
        <meta charset="utf-8">
        <meta name="theme-color" content="#68ACC6">
        <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">

        <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/css/bootstrap.min.css" rel="stylesheet">
        <link href="./assets/css/main.css" rel="stylesheet">
        <link href="./assets/css/index.css" rel="stylesheet">

        <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script>
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        <!-- Global site tag (gtag.js) - Google Analytics -->
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    </head>
    <body>
		<div id="top_menu"></div>
		<div class="container">
			<div class="row justify-content-center">
                <div class="col col-12 col-md-9 fs-5 text-center mb-5 mt-5">
                    MIDeL is: MIDeL is a website to help healthcare professionals and medical imaging scientists learn to apply deep learning to medical images. This website consists of a comprehensive text (think of an electronic textbook) integrated with actual code examples to help you learn about Deep Learning. We specifically focus on the MONAI framework, as it provides many nice functions that work well with medical images. This means that you will need to use Python and the PyTorch framework. If that sounds intimidating don’t worry—the first few chapters provide background on programming, as well as the math and medical image fundamentals that some of you may lack. If you know this already great—feel free to skip forward to the chapters where you encounter material that you are not familiar with.
                    <br>
                </div>
			</div>
		</div>
		<footer class="col-12 text-center bg-light text-muted mt-5"></footer>
    </body>
</html>