Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

11.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.0K
Distributed Loads01:19

Distributed Loads

1.1K
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
1.1K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.3K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.3K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

333
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
333
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.7K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.7K
Statgraphics01:10

Statgraphics

485
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
485

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same authorSame journal

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

DigitAb: Domain-Adaptive Cell Type Prediction Method from Light Microscopy Images.

bioRxiv : the preprint server for biology·2026
Same author

Spatially Resolved Banff Tubulitis and Glomerulitis Scoring in Kidney Allograft Biopsies via Artificial Intelligent -Based Structure Segmentation and Spatial Transcriptomics.

bioRxiv : the preprint server for biology·2026
Same author

Fusion Annotator: A Platform for Accelerating Consensus-Driven Ground Truth Generation with AI Assistance.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Pathomic analysis of 5-year surveillance biopsies as predictors of kidney allograft loss.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons·2026
Same author

AI-Based Digital Pathology-Enabled Spatial-Omics Data Analyses of the Human Kidney.

Journal of proteome research·2026
Same journal

AVA: Automated Viewability Analysis for Ureteroscopic Intrarenal Surgery.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Kidney Endoscopy Video to Preoperative CT Alignment for Depth Estimation.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Reconstructing physiological signals from fMRI across the adult lifespan.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Axially Swept Light-Sheet Microscopy using scattering and fluorescence contrast mechanisms.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts.

Proceedings of SPIE--the International Society for Optical Engineering·2026
查看所有相关文章

相关实验视频

Updated: May 1, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

9.0K

康普雷普斯2.0:在用于组织病理数据处理的高性能计算集群上实现大规模分布式计算.

Suhas Katari Chaluva Kumar1,2, Anindya S Paul1, Haitham Abdelazim1

  • 1Dept. of Medicine - Section of Quantitative Health, University of Florida, Gainesville, FL.

Proceedings of SPIE--the International Society for Optical Engineering
|March 9, 2026
PubMed
概括
此摘要是机器生成的。

通过整合高性能计算来进行全幻灯片图像 (WSI) 大规模分析,ComPRePS 2.0 增强了计算病理学. 这种人工智能驱动的工具显著提高了病研究的可扩展性和安全性.

关键词:
人工智能/ML图像分析分布式计算 分布式计算通过GPU加速的计算.高性能计算 (HPC) 是一种高性能计算.这就是HyperGator.组织病理学分析分析可扩展的人工智能系统

更多相关视频

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
07:52

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

1.6K
Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
05:22

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

900

相关实验视频

Last Updated: May 1, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

9.0K
Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
07:52

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

1.6K
Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
05:22

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

900

科学领域:

  • 计算病理学计算病理学
  • 数字病理学数字病理学
  • 医疗信息学 医疗信息学

背景情况:

  • 组织学数据的数字化成全幻灯片图像 (WSI) 推动了计算病理学的进步.
  • 大规模的高分辨率图像处理对于分析复杂的生物医学数据,特别是私人患者信息至关重要.
  • 现有的计算病理学工具面临着大规模数据集的可扩展性和安全性的局限性.

研究的目的:

  • 开发一套先进的计算病理学套件 (ComPRePS 2.0) 解决其前身的可扩展性和安全性限制.
  • 利用高性能计算集群 (HPCC) 进行高效处理千兆像素WSI.
  • 增强用于临床任务和研究的组织病理幻灯片的AI驱动分析.

主要方法:

  • 将计算病学套件 (ComPRePS 2.0) 与佛罗里达大学的HyperGator HPCC集成在一起.
  • 使用按需的CPU,GPU和内存资源.
  • 实现基于Apptainer的容器化和用于分布式计算的并行文件系统访问.

主要成果:

  • 与ComPRePS 1.0.0相比,ComPRePS 2.0的性能得到了15倍的提高.
  • 实现了前所未有的可扩展性和增强的安全性,用于处理大维的WSIs.
  • 成功处理了920个大尺寸WSI,为病研究生成了关键数据.

结论:

  • 康普雷普斯2.0提供了一个可扩展和安全的平台,用于自动化组织病理幻灯片分析.
  • 与HPCC集成显著提高了数字病理学工作负载的计算能力.
  • 这一进步促进了大规模数据分析,有利于研究人员,病理学家和学生了解病的进展.