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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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相关实验视频

Updated: Jun 10, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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医学成像机器学习的可解释性框架

Alan Q Wang1,2, Batuhan K Karaman1,2, Heejong Kim2

  • 1School of Electrical and Computer Engineering, Cornell University-Cornell Tech, New York City, NY 10044, USA.

IEEE access : practical innovations, open solutions
|October 18, 2024
PubMed
概括
此摘要是机器生成的。

医学成像 (MLMI) 机器学习中的解释性需要正式化. 本研究定义了五个核心元素和一个框架,以指导MLMI模型的设计和应用,以便更好地在现实世界中使用.

关键词:
可以解释性 解释性可以解释性的解释性.机器学习是机器学习.医学成像医学成像

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科学领域:

  • 医疗成像医学成像
  • 机器学习 机器学习
  • 医疗保健中的人工智能

背景情况:

  • 医学成像 (MLMI) 机器学习中的可解释性至关重要,但缺乏明确的定义.
  • 现有的方法往往缺乏对可解释性目标和组件的结构化理解.

研究的目的:

  • 在MLMI背景下正式确定可解释性的目标和元素.
  • 开发一个框架,指导可解释的MLMI模型的设计和应用.

主要方法:

  • 关于现实世界的医学图像分析任务和机器学习交叉点的推理.
  • 识别和定义MLMI可解释性的五个核心元素:本地化,视觉识别,物理归属,模型透明度和可操作性.

主要成果:

  • 一个正式的MLMI解释性框架,概述了一步一步的方法.
  • 确定五个关键要素,这些要素对于实际的MLMI解释性至关重要.

结论:

  • 拟议的框架澄清了MLMI特定的解释性目标和考虑因素.
  • 这项工作旨在指导从业者和研究人员开发和使用更易于解释的MLMI模型,以改善临床影响.