Jove
Visualize
Contact Us

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 Leukocytes01:30

Classification of Leukocytes

3.3K
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...
3.3K
Classification of Illness01:17

Classification of Illness

8.0K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.0K
Classification of Signals01:30

Classification of Signals

980
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...
980
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

3.8K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
3.8K
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

You might also read

Related Articles

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

Sort by
Same author

New understanding of the molecular pathogenesis and therapeutic progress of hepatocellular carcinoma.

Oncology reviews·2026
Same author

A feasibility study of DNA ploidy analysis, HPV, and TCT for screening of cervical cancer: A retrospective study.

Medicine·2024
Same author

A rare presentation of central pontine myelinolysis secondary to hyperglycaemia.

BMC endocrine disorders·2023
Same author

Optimization and Developmental Validation of 38-plex InDel Panel for Ancestry Inference.

Fa yi xue za zhi·2023
Same author

Annexin A2 degradation contributes to dopaminergic cell apoptosis via regulating p53 in neurodegenerative conditions.

Neuroreport·2021
Same author

Assessment of a biofluid mechanics-based model for calculating portal pressure in canines.

BMC veterinary research·2020
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 Experiment Video

Updated: Sep 29, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.0K

MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification.

Cheng-Fan Li1, Yi-Duo Xu1, Xue-Hai Ding1

  • 1School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China.

Computers in Biology and Medicine
|March 19, 2022
PubMed
Summary

This study introduces MultiR-Net, a deep learning model for diagnosing COVID-19 using chest CT scans. It accurately classifies COVID-19 and segments lesions, improving diagnostic efficiency for healthcare professionals.

Keywords:
COVID-19InterpretabilityMulti-scaleMulti-task learningV-Net

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

538
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.7K

Related Experiment Videos

Last Updated: Sep 29, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.0K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

538
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.7K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • COVID-19 diagnosis relies heavily on chest CT scans, but distinguishing it from other pneumonias is challenging.
  • Current deep learning models often focus on either classification or segmentation, neglecting feature correlation.

Purpose of the Study:

  • To develop a novel 3D deep learning model, MultiR-Net, for combined COVID-19 classification and lesion segmentation.
  • To enable real-time and interpretable COVID-19 chest CT diagnosis.

Main Methods:

  • Proposed a MultiR-Net model with two subnets: a UNet-like subnet for segmentation and a classification subnet.
  • Employed a reverse attention mechanism and iterable training strategy for feature fusion between subnets.
  • Introduced a novel loss function to enhance inter-subnet interaction.

Main Results:

  • Achieved an average recall of 93.323% and precision of 94.005% for COVID-19 classification.
  • Obtained a 69.95% Volume Dice score for lesion segmentation.
  • Demonstrated effectiveness on a dataset of 275 3D CT scans including COVID-19, CAP, and healthy cases.

Conclusions:

  • The proposed MultiR-Net effectively integrates classification and segmentation for improved COVID-19 diagnosis.
  • The multi-task learning approach enhances diagnostic accuracy and interpretability in chest CT analysis.