创新医学影像学课程

IF 0.7 Q3 MULTIDISCIPLINARY SCIENCES
Christopher Wiedeman, Huidong Xie, X. Mou, Ge Wang
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引用次数: 0

摘要

人工智能(AI)和机器学习(ML),特别是深度学习,已经在整个社会产生了巨大的影响,包括断层医学成像领域。计算机视觉和图像分析是深度学习和处理现有图像的主要应用实例,与之相反,断层医学成像主要通过传感器测量产生内部结构的横截面或体积图像。最近,深度学习在世界范围内开始积极发展,用于医学成像,包括断层成像重建和图像分析。虽然医学成像是一个成熟的领域,在过去的几十年里积累了丰富的教学经验,但鉴于基于人工智能的医学成像领域,特别是深度断层成像领域的快速变化,更新医学成像课程以反映AI/ML的影响是一个新的挑战。在2019年秋季学期,伦斯勒理工学院(Rensselaer Polytechnic Institute)的医学成像课程进行了修改,加入了一个人工智能框架,并得到了学生的积极反馈。令人鼓舞的是,正如他们的调查报告所证实的那样,许多学生在课堂上和实践项目中表现出了学习人工智能的强烈动机。在2020年秋季学期,我们进一步完善了这门课程,纳入了新的进展。这篇文章描述了我们的教学理念、实践以及关于整合深度学习、层析成像和动手编程来重新定义医学成像课程的考虑。此外,鉴于疫情持续蔓延,在线教学和考试已成为高等教育的一个组成部分。这些需要也将得到解决,希望将来能开设一门公开课程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Innovating the Medical Imaging Course
Artificial intelligence (AI) and machine learning (ML), especially deep learning, have generated tremendous impacts throughout our society, including the tomographic medical imaging field. In contrast to computer vision and image analysis, which have been major application examples of deep learning and deal with existing images, tomographic medical imaging mainly produces cross-sectional or volumetric images of internal structures from sensor measurements. Recently, deep learning has started being actively developed worldwide for medical imaging, including both tomographic reconstruction and image analysis. While medical imaging is a well-established field, in which extensive teaching experience has been accumulated over the past few decades, updating the medical imaging course to reflect AI/ML influence is a new challenge given the rapidly changing landscape of AI-based medical imaging, particularly deep tomographic imaging. In the 2019 fall semester, the medical imaging course at Rensselaer Polytechnic Institute was modified to include an AI framework with positive feedback from students. Encouragingly, many students showed a strong motivation to learn AI in classes and hands-on projects, as confirmed in their survey reports. In the 2020 fall semester, we improved this course further, incorporating new advances. This article describes our teaching philosophy, practice, and considerations with respect to integrating deep learning, tomographic imaging, and hands-on programming to redefine the medical imaging course. Furthermore, given the persistent pandemic, online teaching and examination have become an integral part of higher education. These needs will be addressed as well, with the hope of developing an open course in the future.
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来源期刊
Technology and Innovation
Technology and Innovation MULTIDISCIPLINARY SCIENCES-
自引率
20.00%
发文量
12
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