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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

4.9K
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...
4.9K
Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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Quantitative Impact of T1 Subtraction Maps on Enhancing Component Delineation and Measured Volumes in Minimally Enhancing Pediatric Brain Tumors.

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Phase 1 trial of pre-operative image guided intensity modulated photon radiotherapy with simultaneously integrated boost to the high-risk margin for patients with retroperitoneal sarcoma.

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Artificial intelligence analysis of temporalis muscle thickness for monitoring sarcopenia and clinical outcomes in individuals with paediatric brain tumours: a retrospective cohort study.

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Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma.

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Predicting Chemotherapy Response from Staging Laparoscopy Images.

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Development and External Validation of a Machine Learning Model for 10-Year Ischemic Stroke Risk Prediction in Diverse Populations.

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相关实验视频

Updated: Jun 5, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

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一个广义的大脑MRI分析的基础模型.

Divyanshu Tak1,2, Biniam A Garomsa1,2, Tafadzwa L Chaunzwa1,2,3

  • 1Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States.

medRxiv : the preprint server for health sciences
|December 16, 2024
PubMed
概括
此摘要是机器生成的。

一个新的基础模型,脑成像自适应核心 (BrainIAC),增强人工智能 (AI) 用于脑磁共振成像 (MRI). 脑IAC可以改善疾病诊断和生物标志物发现,即使数据有限.

关键词:
人工智能的人工智能大脑MRI 脑部MRI 脑部深度学习是一种深度学习.基金会模型 基金会模型自主监督学习学习

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

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相关实验视频

Last Updated: Jun 5, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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科学领域:

  • 神经成像是一种神经成像.
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 人工智能 (AI) 在脑磁共振成像 (MRI) 中显示出疾病诊断和管理的前景.
  • 目前的人工智能模型面临的局限性是由于受限的培训数据和在各种临床场景和患者群体中不良的概括.
  • 基础模型通过利用自我监督学习,预训练和适应提供了一个潜在的解决方案.

研究的目的:

  • 介绍大脑成像自适应核心 (BrainIAC),这是大脑MRI的新型基础模型.
  • 为了使从未标记的大脑MRI数据中实现概括的表示学习.
  • 作为神经成像中各种下游AI应用的基础模型.

主要方法:

  • 开发了脑成像自适应核心 (BrainIAC),这是一个在未标记的大脑MRI数据上训练的基础模型.
  • 雇员自主监督学习,预培训和有针对性的适应策略.
  • 在48,519个大脑MRI上验证了该模型,涉及广泛的任务.

主要成果:

  • 与局部监督训练和其他预训练模型相比,BrainIAC表现优越.
  • 该模型特别擅长在数据不足的系统和复杂的任务中,而其他方法却失败了.
  • 在以前由于数据限制而被认为是不可行的场景中实现了性能改进.

结论:

  • 脑IAC代表了脑MRI人工智能的重大进步,克服了当前特定任务模型的局限性.
  • 基础模型促进了改进的生物标志物发现,并加速了AI的临床翻译.
  • BrainIAC的适应性使其能够集成到现有的成像管道和多模式框架中,以获得更广泛的临床实用性.