Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Parallel Processing

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Report on the 2025 DICOM WSI Connectathon.

Journal of pathology informatics·2026

Same author

Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving Telemedicine.

IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society·2026

Same author

How Laboratory Innovations Are Shaping the Future of Multiple Myeloma Care.

Cancers·2026

Same author

Social Media, Medical Students, and Patient Exposure: Perceptions and Attitudes.

The Linacre quarterly·2026

Same author

Predictors and prognostic impact of dynamic left ventricular outflow tract obstruction during dobutamine stress echocardiography.

The international journal of cardiovascular imaging·2026

Same author

A Plugin-Based Architecture for Integrating AI Services in an Open-Source PACS.

Journal of imaging informatics in medicine·2026

Search research articles

Related Experiment Video

Updated: Sep 22, 2025

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Intra-Query Parallelism for a Scalable and Responsive Web-Based Digital Pathology Viewer.

Rui Jesus¹, Luís Bastião Silva^2,3, Carlos Costa²

¹University of A Coruña, Spain.

Studies in Health Technology and Informatics

|May 25, 2022

Summary

This summary is machine-generated.

Digital pathology imaging faces storage and access challenges. This study introduces a scalable architecture using intra-query parallelism for efficient, dynamic retrieval of large image datasets in distributed systems.

Keywords:

Digital Pathology Distributed systems Intra-Query Parallelism PACS WSI

More Related Videos

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

Related Experiment Videos

Last Updated: Sep 22, 2025

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

Area of Science:

Medical Informatics
Digital Pathology
Health Informatics

Background:

Computational systems are widely adopted in healthcare, yet digital imaging in pathology lags behind radiology.
Inter-institutional web platforms for pathology imaging face challenges in interoperability, data storage, and efficient access to multi-gigabyte images.
Remote access to large pathology image files in heterogeneous environments remains a significant technical hurdle.

Purpose of the Study:

To propose a novel, scalable, and efficient architecture for storing and dynamically retrieving data from distributed large-scale systems.
To address the challenges of data storage, access, and remote visualization in digital pathology.
To facilitate efficient data retrieval for large digital pathology image datasets.

Main Methods:

Development of a scalable architecture for distributed large-scale systems.
Implementation of dynamic data retrieval mechanisms.
Utilization of intra-query parallelism for efficient retrieval of numerous image segments.

Main Results:

The proposed architecture demonstrates scalability and efficiency in storing and retrieving large-scale digital pathology image data.
Intra-query parallelism enables effective retrieval of multiple image segments within a distributed environment.
The system facilitates dynamic data access, overcoming previous limitations in remote visualization.

Conclusions:

The developed architecture provides a robust solution for managing and accessing large digital pathology image datasets.
The methodology enhances data retrieval efficiency, paving the way for broader adoption of digital imaging in pathology.
This work contributes to overcoming key technological barriers in digital pathology, improving data accessibility and utilization.