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

You might also read

Related Articles

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

Sort by

Same author

EcoBOT: an AI/ML enabled automated phenotyping capability for model plants.

Frontiers in plant science·2025

Same author

Optimizing inference of segmentation on high-resolution images in MLExchange.

The Journal of supercomputing·2025

Same author

High-Performance Hybrid-Global-Deflated-Local Optimization with Applications to Active Learning.

Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing : XLOOP. Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing·2024

Same author

DLSIA: Deep Learning for Scientific Image Analysis.

Journal of applied crystallography·2024

Same author

Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities.

IS&T International Symposium on Electronic Imaging·2023

Same author

MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies.

Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing : XLOOP. Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing·2023

Search research articles

Related Experiment Video

Updated: May 15, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

qlty: handling large tensors in scientific imaging deep-learning workflows.

Petrus H Zwart^1,2,3

¹Center for Advanced Mathematics in Energy Research Applications, Lawrence Berkeley National Laboratory.

Software Impacts

|April 10, 2025

Summary

This toolkit addresses challenges in deep learning for scientific imaging by managing large datasets. It enables effective analysis of volumetric data on resource-limited systems.

Keywords:

deep learning denoising machine learning segmentation

More Related Videos

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: May 15, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Scientific Imaging
Deep Learning
Data Science

Background:

Deep learning is crucial for scientific image analytics.
Large volumetric datasets pose memory challenges for GPUs.
Efficient handling of big data is needed for deep learning applications.

Purpose of the Study:

Introduce qlty, a toolkit for managing large-scale spatial data.
Enable deep learning on resource-limited hardware.
Facilitate effective training and inference for volumetric datasets.

Main Methods:

Tensor management techniques.
Subsampling methods for large datasets.
Data cleaning and stitching algorithms.

Main Results:

qlty provides robust tools for data preprocessing.
Enables deep learning workflows with large volumetric data.
Facilitates efficient analysis in memory-constrained environments.

Conclusions:

qlty effectively addresses memory limitations in deep learning for scientific imaging.
The toolkit supports scalable analysis of large spatial datasets.
It democratizes access to advanced image analytics on standard hardware.