Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

299
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
299
Classification of Systems-II01:31

Classification of Systems-II

133
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
133
Classification of Systems-I01:26

Classification of Systems-I

168
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
168
Structural Classification of Joints01:20

Structural Classification of Joints

3.1K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.1K

You might also read

Related Articles

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

Sort by
Same author

SUMOylation in Mitochondrial Quality Control: Mechanisms and Implications for Neurodegenerative Disease.

Molecular neurobiology·2026
Same author

Evaluation of the RTyper Y27 chip amplification system with Quick TargSeq 1.0 for Y-STR genotyping in forensic science.

International journal of legal medicine·2026
Same author

Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.

Medical image analysis·2026
Same author

Exploring the relationship between preferred bubble tube speeds in sensory rooms and physiological-psychological factors: A study on interoceptive sensitivity, subjective time perception, visual discomfort levels, and anxiety levels.

F1000Research·2026
Same author

Integrated multi-technology exploration of the mechanism by which Badushengji San regulates core targets in diabetic foot ulcer.

Molecular genetics and genomics : MGG·2026
Same author

A multimodal vision dataset for nursing action recognition and quality assessment in NICU.

Scientific data·2026

Related Experiment Video

Updated: May 30, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

455

Dynamic graph based weakly supervised deep hashing for whole slide image classification and retrieval.

Haochen Jin1, Junyi Shen2, Lei Cui3

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.

Medical Image Analysis
|January 29, 2025
PubMed
Summary

This study introduces a deep hashing framework for whole slide images (WSIs) that improves classification and enables retrieval by considering patch relationships. The novel method enhances performance on clinical diagnostic tasks.

Keywords:
Attention-based MILDynamic graphHashing encodingWhole slide images

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K

Related Experiment Videos

Last Updated: May 30, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

455
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K

Area of Science:

  • Computational pathology
  • Deep learning for medical imaging
  • Multiple instance learning

Background:

  • Existing methods for whole slide image (WSI) analysis using deep multiple instance learning (MIL) primarily focus on classification.
  • These methods often overlook the spatial relationships between image patches, potentially limiting performance.
  • The ability to perform retrieval tasks is crucial for clinical diagnosis but is not adequately addressed by current MIL approaches.

Purpose of the Study:

  • To develop a novel end-to-end deep hashing framework for WSIs.
  • To simultaneously address both classification and retrieval tasks within a unified model.
  • To overcome the limitations of existing MIL methods by incorporating patch relationships and enabling retrieval.

Main Methods:

  • A multi-scale representation attention deep network was employed as the backbone to extract patch-level features from WSIs.
  • A novel patch-based dynamic graph construction method was introduced to learn inter-patch relationships within each image.
  • Hashing encoding layers were utilized to convert patch- and WSI-level features into binary codes for retrieval.

Main Results:

  • The proposed framework demonstrated superior performance on both classification and retrieval tasks compared to state-of-the-art methods across multiple datasets.
  • The integration of patch relationships through dynamic graphs significantly enhanced the model's analytical capabilities.
  • The framework successfully enabled both patch-level and WSI-level image retrieval.

Conclusions:

  • The novel MIL-based deep hashing framework effectively handles both classification and retrieval of WSIs.
  • Incorporating patch relationships and utilizing hashing significantly advances WSI analysis for clinical applications.
  • The proposed method offers a promising solution for improving diagnostic accuracy and efficiency in digital pathology.