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

Classification of Epithelial Tissues: Stratified Epithelium

Classification of Epithelial Tissues: Stratified Epithelium

Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...

You might also read

Related Articles

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

Sort by

Same author

Generative adversarial networks-based Image-to-Image translation allows tumor consistency prediction from standard MR images in pituitary adenomas.

PLOS digital health·2026

Same author

Outcomes of orbital-cavernous sinus schwannomas via pterional approach combined with orbital osteotomy: a retrospective study of 32 cases.

Neurosurgical review·2026

Same author

Clinical predictors of postoperative cerebrospinal fluid leakage following endoscopic endonasal surgery for pituitary neuroendocrine tumors: a retrospective cohort study.

BMC neurology·2026

Same author

Regulating open pores to engineer closed pores in ZIF-8-derived hard carbon for high-plateau-capacity sodium-ion batteries.

Journal of colloid and interface science·2025

Same author

Concentration Gradient Driving Rapid Potassium Ion Diffusion in Graphite.

ACS applied materials & interfaces·2025

Same author

Spheno-Orbital Aneurysmal Bone Cyst in Children: A Report of Three Cases and Literature Review.

World neurosurgery·2024

Same journal

Anti-aliasing-enhanced WaveUNet for clinically reliable 12-lead ECG reconstruction from limited 3-lead input.

Medical & biological engineering & computing·2026

Same journal

Deep multi-modal features based spatio-temporal video regression for non-invasive hemoglobin estimation.

Medical & biological engineering & computing·2026

Same journal

Reduced mechanical strength correlates with decreased elastin content in aortic intima-media tissue: association with dissection in human ascending aortas.

Medical & biological engineering & computing·2026

Same journal

How plaque morphology and stenosis severity govern stent-artery interaction and deployment outcomes: a computational study.

Medical & biological engineering & computing·2026

Same journal

Investigating a relation between amyloid beta plaque burden and accumulated neurotoxicity caused by amyloid beta oligomers.

Medical & biological engineering & computing·2026

Same journal

A robot-assisted eye positioning method with high precision and repeatability for ocular particle therapy: mechanical and geometric assessment.

Medical & biological engineering & computing·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 22, 2025

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Published on: June 2, 2023

Reimagining cancer tissue classification: a multi-scale framework based on multi-instance learning for whole slide

Zixuan Wu¹, Haiyong He², Xiushun Zhao³

¹School of Automation, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China.

Medical & Biological Engineering & Computing

|March 15, 2025

Summary

This summary is machine-generated.

This study introduces an integrated framework for Whole Slide Image (WSI) analysis in cancer pathology. The novel approach enhances tumor detection accuracy by effectively handling data challenges and improving feature recognition.

Keywords:

Multi-scale feature fusion Multiple instance learning Similarity focal loss Whole slide image classification

More Related Videos

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

Published on: September 22, 2013

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

Related Experiment Videos

Last Updated: May 22, 2025

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Published on: June 2, 2023

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

Published on: September 22, 2013

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

Area of Science:

Computational pathology
Digital pathology
Machine learning in oncology

Background:

Whole Slide Image (WSI) analysis in cancer pathology faces challenges including data invalidity, scale variations, and difficult samples.
Existing Multiple Instance Learning (MIL) frameworks struggle to address these issues concurrently.

Purpose of the Study:

To develop an integrated recognition framework for WSI-based cancer pathology diagnosis that overcomes limitations of current MIL methods.
To improve the accuracy and efficiency of tumor detection in digital pathology.

Main Methods:

Proposed a three-component framework: a preprocessing selection method for representative patch identification, an Efficient Feature Pyramid Network (EFPN) for multi-instance learning with multi-scale feature extraction, and a Similarity Focal Loss for enhanced discrimination.
EFPN captures diverse tissue features by building a multi-scale feature pyramid, mimicking pathologist's diagnostic approach.
Similarity Focal Loss refines classification by focusing on challenging samples and boundary information.

Main Results:

Achieved high test accuracies for binary tumor classification: 93.58% on CAMELYON16, 84.74% on one private dataset, and 99.91% on another.
The integrated framework demonstrated superior performance compared to existing techniques across all tested datasets.

Conclusions:

The proposed integrated framework effectively addresses key challenges in WSI analysis for cancer pathology.
This novel approach significantly enhances the accuracy and generalization of MIL-based tumor classification in digital pathology.