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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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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
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Related Experiment Video

Updated: Jan 17, 2026

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

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Automated Skin Cancer Report Generation via a Knowledge-Distilled Vision-Language Model.

Lawhori Chakrabarti1, Boyu Zhang1, Hengyi Tian1

  • 1Department of Computer Science, University of Idaho, Moscow, ID 83844, USA.

IEEE Access : Practical Innovations, Open Solutions
|September 19, 2025
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) can now generate detailed skin cancer diagnostic reports from dermoscopic images. This breakthrough enhances transparency and reduces clinician workload in dermatology.

Keywords:
Melanomadermoscopy imageexplainable AIknowledge distillationmedical report generationskin cancervision-language models

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Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Artificial Intelligence (AI) shows promise in analyzing dermoscopic images for skin cancer diagnostics.
  • Lack of transparency and interpretability in AI diagnostic tools hinders clinical adoption.
  • Automated report generation can improve AI explainability and reduce medical professional workload.

Purpose of the Study:

  • To develop a multimodal vision-language model (VLM) for generating structured medical reports from dermoscopic images.
  • To enhance the interpretability and clinical utility of AI-driven dermatological diagnostics.
  • To bridge the gap between AI capabilities and clinical needs through automated report generation.

Main Methods:

  • A two-stage knowledge distillation (KD) framework was employed to train the VLM.
  • The model generates reports structured into Findings, Impression, and Differential Diagnosis sections.
  • Reports incorporate descriptive features based on the 7-point melanoma checklist.

Main Results:

  • The VLM successfully produced accurate and interpretable dermatological reports.
  • Human feedback confirmed the clinical relevance, completeness, and interpretability of the generated reports.
  • Computational metrics (SacreBLEU, ROUGE-1, ROUGE-L, BERTScore F1) validated report accuracy and alignment.

Conclusions:

  • The multimodal VLM effectively generates structured, clinically relevant reports from dermoscopic images.
  • The system's explainability and generalization capabilities are supported by its design and validation.
  • This approach offers a significant advancement in AI-assisted skin cancer diagnostics.