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

You might also read

Related Articles

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

Sort by

Same author

Efficacy, tolerability, and EEG lateralization-based predictors of neuropsychiatric adverse events in pediatric SeLECTS treated with perampanel monotherapy.

Seizure·2026

Same author

Imbalanced mitochondrial fusion/fission in human placenta: a potential link to preeclampsia.

Scientific reports·2026

Same author

Development of an anemia detection model in emergency departments using lip region images based on medical knowledge and deep learning technology.

Scientific reports·2026

Same author

Efficacy and safety of allogeneic umbilical cord blood mononuclear cells therapy in children with autism spectrum disorder and immune dysregulation: a single-center, double-blinded, randomized, placebo-controlled trial.

Stem cell research & therapy·2026

Same author

Exploring the anti-gout mechanism of Erding granules based on network pharmacology and experimental verification.

Pakistan journal of pharmaceutical sciences·2026

Same author

Generation of iPSC line from peripheral blood mononuclear cells of a patient with Duchenne muscular dystrophy.

Stem cell research·2026

Search research articles

Related Experiment Video

Updated: Sep 1, 2025

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

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

Published on: April 14, 2023

SLT-Net: A codec network for skin lesion segmentation.

Kaili Feng¹, Lili Ren², Guanglei Wang¹

¹The School of Electronic Information Engineering, Hebei University, Hebei, 071002, China.

Computers in Biology and Medicine

|August 14, 2022

Summary

This summary is machine-generated.

A new skin lesion Transformer network (SLT-Net) improves melanoma diagnosis by accurately segmenting skin lesions. This advanced deep learning model enhances segmentation accuracy, outperforming existing methods on public datasets.

Keywords:

Convolutional neural networks Skin lesion segmentation Skip connection Transformer

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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

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

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Sep 1, 2025

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

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

Published on: April 14, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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

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

Published on: November 30, 2022

Area of Science:

Medical image analysis
Computer vision
Artificial intelligence in dermatology

Background:

Accurate skin lesion segmentation is crucial for melanoma diagnosis but challenging due to variations in lesion size, shape, and low contrast.
Traditional convolutional neural networks struggle with multi-scale context and feature interaction, limiting segmentation performance.

Purpose of the Study:

To propose a novel skin lesion segmentation method, the skin lesion Transformer network (SLT-Net).
To enhance the accuracy and efficiency of automatic skin lesion segmentation for improved melanoma diagnosis.

Main Methods:

Developed SLT-Net, a novel codec structure utilizing CSwinUnet for long-distance feature dependence modeling.
Integrated a multi-scale context Transformer (MCT) for inter-level skip connection information interaction in the channel dimension.

Main Results:

Achieved high Dice Similarity Coefficient (DSC) values: 90.45% on ISIC-2016, 79.87% on ISIC-2017, and 82.85% on ISIC-2018.
Demonstrated superior performance compared to most state-of-the-art methods on three public skin lesion datasets.

Conclusions:

SLT-Net significantly improves skin lesion segmentation accuracy.
The proposed method sets a new benchmark for skin lesion segmentation tasks, aiding in melanoma diagnosis.