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相关实验视频

Updated: Sep 19, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

基于年龄的,基于注意力的弱监督学习用于神经病理图像评估.

Shuying Li¹, Maxwell Malamut¹, Ann McKee^2,3,4,5

¹Department of Electrical & Computer Engineering, Boston University, Boston MA 02215, USA.

bioRxiv : the preprint server for biology

|June 6, 2025

概括

此摘要是机器生成的。

这项研究引入了一个人工智能管道,通过分析大脑组织图像来诊断诸如CTE等神经退行性疾病. 该模型准确地预测病理标志物,有助于更早,更精确的诊断.

关键词:

慢性创伤性脑病 (CTE) 是一种慢性创伤性脑病.数字病理学数字病理学基金会模型基金会模型多个实例的学习.神经病理学神经病理学弱监督的学习学习弱监督的学习整个幻灯片图像图像

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Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains

Published on: June 3, 2020

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

相关实验视频

Last Updated: Sep 19, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains

Published on: June 3, 2020

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

科学领域:

神经病理学神经病理学
数字病理学数字病理学
人工智能在医学中的应用

背景情况:

诊断神经退行性疾病 (NDs),如慢性创伤性脑病变 (CTE) 是具有挑战性的,因为微妙的病理变化.
手动组织病理学分析是耗时的,可变的,可能会错过疾病的早期迹象.

研究的目的:

开发一个自动化的,以年龄为基础的计算管道,用于预测NDs中的tau病理.
为了提高诊断的准确性和识别神经退行症的微妙结构变化.

主要方法:

使用Luxol Fast Blue和LH&E染色全幻灯片图像 (WSIs) 开发了一个基于注意力的多实例学习 (MIL) 管道.
该模型预测AT8密度 (p-tau聚合的标志物),并纳入患者年龄以提高准确性.
建立了基础模型 (FMs) 的定量评估程序,评估注意力地图属性和稳定性.

主要成果:

基于年龄的MIL管道准确地确定了关键的病理区域,并预测了AT8密度.
可解释的注意力图突出显示了与病理相关的结构变化.
开发的基准表明了该模型对染色和噪声变化的稳定性.

结论:

开发的管道使得可扩展的,自动化WSI分析ND诊断.
这种方法支持更早,更精确地检测CTE和其他NDs.
该研究为评估和优化数字神经病理学的基础模型提供了工具.