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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.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Related Experiment Video

Updated: May 25, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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A multi-stage multi-modal learning algorithm with adaptive multimodal fusion for improving multi-label skin lesion

Lihan Zuo1, Zizhou Wang2, Yan Wang2

  • 1School of Computer and Artificial Intelligence, Southwest Jiaotong University, Chengdu 610000, PR China.

Artificial Intelligence in Medicine
|February 27, 2025
PubMed
Summary

This study introduces a new deep learning method for skin cancer diagnosis using clinical images, dermoscopy images, and metadata. The hybrid fusion strategy improves diagnostic accuracy by adaptively combining multimodal information.

Keywords:
Multi-label classificationMulti-modal information fusionMulti-modal learningSkin lesion classification

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Oncology

Background:

  • Skin cancer is a significant global health concern, contributing to cancer incidence and mortality.
  • Accurate and timely diagnosis is crucial for effective skin cancer treatment and patient survival.
  • Current deep learning methods for skin cancer screening often use single-modality inputs, limiting diagnostic accuracy.

Purpose of the Study:

  • To develop a novel multi-modal learning algorithm for skin cancer diagnosis.
  • To introduce an uncertainty-based hybrid fusion strategy to enhance diagnostic accuracy.
  • To adaptively combine information from clinical images, dermoscopy images, and metadata.

Main Methods:

  • A hybrid fusion strategy combining intermediate and late fusion techniques was developed.
  • Cosine similarity and concatenation were used to fuse clinical and dermoscopy image features.
  • Uncertainty-based late fusion was employed to integrate image and metadata modalities.

Main Results:

  • The proposed method demonstrated effectiveness in a comprehensive experimental evaluation on a public skin disease dataset.
  • The hybrid fusion strategy successfully leveraged complementary and correlated information from multiple modalities.
  • The uncertainty mechanism allowed for adaptive and confident fusion of diverse data types.

Conclusions:

  • The developed uncertainty-based hybrid fusion algorithm significantly enhances automated skin lesion classification.
  • This approach offers a more robust and adaptive solution for skin cancer diagnosis compared to single-modality methods.
  • The findings suggest improved clinical applicability for automated skin cancer detection systems.