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

Nuclear Fusion

Nuclear Fusion

The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons.
A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Encoding

Encoding

Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...

Molecular Shape and Polarity

Molecular Shape and Polarity

Dipole Moment of a Molecule

Network Covalent Solids

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

VSEPR Theory and the Basic Shapes

VSEPR Theory and the Basic Shapes

Overview of VSEPR Theory

You might also read

Related Articles

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

Sort by

Same author

Recent advances in microfluidic technologies for circulating tumor cell isolation: from separation strategies to clinical translation.

Analytical methods : advancing methods and applications·2026

Same author

Lateral flow immunochromatographic assay (LFIA) for tumor marker detection: technical evolution, clinical translation challenges, and future perspectives.

Mikrochimica acta·2026

Same author

Advantages of magnetic compression in rat colonic anastomosis: a comparative study with a transcriptomic perspective.

Scientific reports·2026

Same author

Targeting WIP1 reprograms immunosuppressive tumor microenvironment to potentiate immunotherapy response in colorectal cancer.

Cell death and differentiation·2026

Same author

Targeting endolysosomal acidification inhibits poxvirus entry and replication.

Cell communication and signaling : CCS·2026

Same author

Transcriptomics uncovers the advantages of magnetic compression technique in gastrointestinal anastomosis repair and regeneration.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract·2026

Search research articles

Related Experiment Video

Updated: Jan 29, 2026

Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion

Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion

Published on: April 13, 2019

DEW-Net: A W-Shaped Dual-Encoder Network with Attention Fusion Mechanisms for Pathological H&E Image Segmentation.

Fuhan Meng, Xixiang Deng, Yingbo Qu

IEEE Journal of Biomedical and Health Informatics

|January 27, 2026

Summary

A new DEW-Net model accurately segments programmed cell death-ligand 1 (PD-L1) expression in lung cancer images. This method improves segmentation accuracy by effectively fusing local and global features, outperforming existing approaches.

More Related Videos

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

Published on: October 4, 2018

A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets

A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets

Published on: December 5, 2014

Related Experiment Videos

Last Updated: Jan 29, 2026

Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion

Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion

Published on: April 13, 2019

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

Published on: October 4, 2018

A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets

A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets

Published on: December 5, 2014

Area of Science:

Digital pathology
Medical image analysis
Computational oncology

Background:

Accurate segmentation of programmed cell death-ligand 1 (PD-L1) expression in lung squamous cell carcinoma (LSCC) is crucial for treatment decisions.
Pathological H&E images present challenges due to morphological heterogeneity and varied expression area sizes.
Existing hybrid CNN-Transformer models struggle with effective information fusion, limiting PD-L1 segmentation accuracy.

Purpose of the Study:

To develop an advanced deep learning model for precise pixel-level segmentation of PD-L1 expression in LSCC.
To enhance information interaction and reduce redundant data during feature fusion in pathological image analysis.
To improve the accuracy and generalization capability of PD-L1 segmentation models.

Main Methods:

Proposed a W-shaped dual-encoder network (DEW-Net) integrating a CNN encoder and a Swin Transformer encoder in parallel.
Introduced a Cross-Attention Fusion (CAF) module for improved semantic feature fusion and information interaction.
Incorporated Channel Attention (CA) and Bilateral-voting Position Attention (BPA) modules to refine feature representation and reduce noise.

Main Results:

DEW-Net achieved superior performance on PD-L1 segmentation tasks across four datasets.
The model reached a Dice Similarity Coefficient (DSC) of 79.93% and an Intersection over Union (IoU) of 71.27% on the PD-L1 segmentation dataset.
Demonstrated significant improvements over state-of-the-art (SOTA) methods in segmentation accuracy and generalization.

Conclusions:

The proposed DEW-Net effectively addresses the challenges of PD-L1 segmentation in LSCC pathological images.
Novel attention fusion mechanisms enhance feature interaction and reduce redundant information, leading to higher segmentation accuracy.
DEW-Net shows strong potential for clinical application in precision oncology and biomarker analysis.