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

You might also read

Related Articles

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

Sort by
Same author

Genomic Landscape of GNAQ and GNA11 Mutations in Metastatic Solid Tumors: A Real-World Data Analysis.

JCO precision oncology·2026
Same author

Deep learning-based arterial waveform analysis for predicting postoperative cerebrovascular events in pediatric patients with Moyamoya disease.

PloS one·2026
Same author

Two-Step Ensemble Convolutional Neural Networks for Colonoscopic Biopsy Classification Resembling Pathologists' Process.

Journal of Korean medical science·2026
Same author

Data augmentation method for computer-aided diagnosis using specular reflection.

Biomedical engineering letters·2026
Same author

Clinical practice guideline for high-risk human papillomavirus testing in cervical cancer screening: a consensus statement from the Korean Society of Gynecologic Oncology.

Journal of gynecologic oncology·2026
Same author

Application of a Natural Language Processing Framework for Data Extraction From Pathology Reports Across Multiple Cancer Types.

Journal of Korean medical science·2026

Related Experiment Video

Updated: Jan 18, 2026

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

4.5K

Large-Scale Dermatopathology Dataset for Lesion Segmentation: Model Development and Analysis.

Yosep Chong1, Daseul Park2, Youngbin Ahn3

  • 1Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul, Korea.

Journal of Korean Medical Science
|September 9, 2025
PubMed
Summary
This summary is machine-generated.

A new, large-scale dermatopathology dataset was created to train artificial intelligence (AI) models for improved skin cancer diagnosis. This high-quality dataset supports AI development for more consistent diagnostic assistance for dermatopathologists.

Keywords:
Deep LearningLarge-Scale Dermatopathology DatasetLesion SegmentationWhole Slide Image

More Related Videos

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

3.3K
SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

535

Related Experiment Videos

Last Updated: Jan 18, 2026

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

4.5K
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

3.3K
SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

535

Area of Science:

  • Dermatopathology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Rising skin cancer incidence increases pathologist workload.
  • Diagnostic variability in skin lesions necessitates AI support.
  • Limited large-scale datasets hinder AI development in dermatopathology.

Purpose of the Study:

  • To build and evaluate a comprehensive dermatopathology image dataset for AI model training.
  • To address the need for large-scale, multi-institutional data in AI-driven diagnostics.

Main Methods:

  • Compiled over 34,376 histopathology images from four institutions.
  • Included normal skin and six common lesion types with annotations.
  • Implemented rigorous data quality management for accuracy and diversity.

Main Results:

  • Dataset achieved high syntactic (0.99) and semantic (0.95) accuracy.
  • Statistical diversity confirmed natural data distribution.
  • Segmentation model demonstrated strong performance (Dice score 80-91%).

Conclusions:

  • The dataset is a valuable resource for deep learning in dermatopathology.
  • Facilitates advancements in AI-assisted diagnostic tools.
  • Supports more consistent and accurate AI-driven dermatopathology diagnoses.