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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
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Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
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Updated: Aug 14, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Intra-class consistency and inter-class discrimination feature learning for automatic skin lesion classification.

Lituan Wang1, Lei Zhang1, Xin Shu1

  • 1Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, PR China.

Medical Image Analysis
|January 13, 2023
PubMed
Summary

This study introduces a deep learning method to improve automated skin lesion classification by enhancing feature consistency and discrimination. The approach boosts diagnostic accuracy for dermoscopic images, addressing challenges in classifying similar and varied lesions.

Keywords:
Class activation mappingInter-class feature discriminationIntra-class feature concentrationSkin lesion classification

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Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Automated skin lesion classification aids dermatologists by analyzing dermoscopic images.
  • Challenges persist due to high intra-class variation and inter-class similarity in lesions.
  • Existing methods struggle to achieve optimal diagnostic performance.

Purpose of the Study:

  • To propose a deep learning method for enhanced automated skin lesion classification.
  • To improve intra-class consistency and inter-class discrimination of learned features.
  • To increase the accuracy of diagnosing skin lesions from dermoscopic images.

Main Methods:

  • A novel deep learning approach incorporating a Class Activation Mapping (CAM)-based global-lesion localization module.
  • Optimization of CAM distances across different lesion tasks to enhance inter-class discrimination.
  • A global features guided intra-class similarity learning module to establish class centers.
  • Utilizing deep features and historical sample features for intra-class concentration.

Main Results:

  • The proposed method demonstrated improved performance on the ISIC-2017 and ISIC-2018 datasets.
  • Experimental results confirmed the method's generalizability across different backbones.
  • The approach effectively focuses on discriminative regions within skin lesions.

Conclusions:

  • The developed deep learning method significantly enhances automated skin lesion classification.
  • The combined strategy of CAM-based discrimination and feature concentration improves diagnostic accuracy.
  • The method shows promise for clinical application in analyzing dermoscopic images.