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

Classification of Signals01:30

Classification of Signals

1.0K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.0K
Classification of Leukocytes01:30

Classification of Leukocytes

4.0K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
4.0K
Force Classification01:22

Force Classification

1.9K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.9K
What is Cell Signaling?02:03

What is Cell Signaling?

125.0K
Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate to respond to the environment.
125.0K

You might also read

Related Articles

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

Sort by
Same author

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Instruction-Guided Scene Text Recognition.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Context Perception Parallel Decoder for Scene Text Recognition.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Genetically predicted 486 blood metabolites in relation to risk of esophageal cancer: a Mendelian randomization study.

Frontiers in molecular biosciences·2024
Same author

SAMGAT: structure-aware multilevel graph attention networks for automatic rumor detection.

PeerJ. Computer science·2024
Same author

Dark horse target Claudin18.2 opens new battlefield for pancreatic cancer.

Frontiers in oncology·2024

Related Experiment Video

Updated: Oct 31, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

301

CRDet: Improving Signet Ring Cell Detection by Reinforcing the Classification Branch.

Zhineng Chen1,2, Sai Wang2,3, Caiyan Jia3

  • 1School of Computer Science, Fudan University, Shanghai, China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|June 30, 2021
PubMed
Summary

Accurate detection of signet ring cells in histopathology images is crucial for cancer grading. A new Classification Reinforcement Detection Network (CRDet) improves detection accuracy by enhancing feature representation, outperforming existing models.

Keywords:
computer-aided diagnosisdigital pathologyobject detectionsignet ring cell

More Related Videos

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

791
Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy
07:29

Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

Published on: May 27, 2020

2.9K

Related Experiment Videos

Last Updated: Oct 31, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

301
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

791
Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy
07:29

Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

Published on: May 27, 2020

2.9K

Area of Science:

  • Computer-aided diagnosis
  • Digital pathology
  • Medical image analysis

Background:

  • Signet ring cell detection is vital for cancer grading and patient survival.
  • Challenges include dense cell distribution, complex visual patterns, and incomplete annotations.
  • Existing object detection models struggle with these complexities.

Purpose of the Study:

  • To develop an improved method for accurate signet ring cell detection in histopathologic images.
  • To address the challenges of dense distribution, diverse patterns, and incomplete annotations.
  • To enhance the performance of computer-aided diagnostic systems for cancer grading.

Main Methods:

  • Proposed a novel Classification Reinforcement Detection Network (CRDet).
  • Integrated a Classification Reinforcement Branch (CRB) into the Cascade RCNN architecture.
  • CRB utilizes a context pooling module for robust feature representation and a feature enhancement classifier with deconvolution and attention mechanisms.

Main Results:

  • CRDet demonstrated superior performance in signet ring cell identification.
  • The model effectively characterized small-sized cells through enhanced features.
  • Outperformed several popular convolutional neural network-based object detection models on a large-scale clinical dataset.

Conclusions:

  • The proposed CRDet effectively mitigates detection difficulties for signet ring cells.
  • Enhanced feature representation using context pooling and attention mechanisms is key to improved accuracy.
  • CRDet offers a promising advancement for computer-aided diagnosis in oncology.