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

400
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...
400
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

57.1K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
57.1K
Classification of Connective Tissues01:30

Classification of Connective Tissues

12.2K
The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense....
12.2K
Sieve Analysis and Grading Curves01:19

Sieve Analysis and Grading Curves

568
Sieve analysis is a method used to determine the particle size distribution of aggregate materials. This process involves the following steps:
568
Types of Aggregate Grading01:15

Types of Aggregate Grading

893
Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
Well-graded aggregates include a complete range of necessary size fractions that fit together to create a dense matrix with minimal voids, represented by a smooth, continuous gradation curve. This type of grading ensures good...
893

You might also read

Related Articles

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

Sort by
Same author

A multi-task cross-attention strategy to segment and classify polyps.

Biomedical physics & engineering express·2025
Same author

A Riemannian multimodal representation to classify parkinsonism-related patterns from noninvasive observations of gait and eye movements.

Biomedical engineering letters·2025
Same author

A deep supervised cross-attention strategy for ischemic stroke segmentation in MRI studies.

Biomedical physics & engineering express·2023
Same author

An inception-based deep multiparametric net to classify clinical significance MRI regions of prostate cancer.

Physics in medicine and biology·2022
Same author

A digital cardiac disease biomarker from a generative progressive cardiac cine-MRI representation.

Biomedical engineering letters·2022

Related Experiment Video

Updated: Sep 29, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

A multitask deep representation for Gleason score classification to support grade annotations.

Fabian León1, Fabio Martínez1

  • 1Biomedical Imaging, Vision and Learning Laboratory (BIVL2ab), Universidad Industrial de Santander, Bucaramanga, Colombia.

Biomedical Physics & Engineering Express
|March 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning approach to improve prostate cancer grading using an embedding representation. The method enhances accuracy in Gleason grading, addressing expert disagreement in cancer progression assessment.

Keywords:
auxiliary taskembedding spacegleason systemtriplet loss

Related Experiment Videos

Last Updated: Sep 29, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

Area of Science:

  • Digital pathology
  • Oncology
  • Machine learning in medicine

Background:

  • The Gleason grading system is crucial for prostate cancer prognosis but suffers from significant inter- and intra-observer variability among pathologists.
  • Current deep learning models for Gleason grading may learn biased stratification rules, limiting their generalizability.
  • Addressing variability is key to developing reliable automated tools for prostate cancer diagnosis.

Purpose of the Study:

  • To develop an embedding representation for prostate cancer histological images that accounts for inter- and intra-observer variability in Gleason grading.
  • To improve the accuracy and robustness of automated Gleason grading using deep learning.
  • To provide a more consistent diagnostic aid for pathologists in prostate cancer assessment.

Main Methods:

  • Implementation of a triplet loss scheme to create a feature embedding space from histological patches.
  • Integration of an auxiliary cross-entropy task to manage inter-class sample variability and enhance feature robustness.
  • Training and evaluation of the deep learning model on prostate cancer histological data.

Main Results:

  • The proposed embedding strategy achieved promising results in Gleason grading.
  • Average accuracy of 66% and 64% was obtained when compared against two independent expert pathologists, with no statistical difference.
  • An average accuracy of 73% was achieved on patches where both pathologists agreed, demonstrating robust pattern learning.

Conclusions:

  • The developed embedding representation with auxiliary task learning effectively addresses the variability inherent in the Gleason grading system.
  • This approach shows potential as a reliable tool to support pathologists in prostate cancer diagnosis and stratification.
  • The method offers a more consistent and accurate alternative to existing automated grading systems.