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Related Experiment Video

Updated: Oct 6, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

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Integrating Domain Knowledge into Deep Learning for Skin Lesion Risk Prioritization to Assist Teledermatology

Rafaela Carvalho¹, Ana C Morgado¹, Catarina Andrade¹

¹Fraunhofer Portugal AICOS, Rua Alfredo Allen, 4200-135 Porto, Portugal.

Diagnostics (Basel, Switzerland)

|January 21, 2022

Summary

This summary is machine-generated.

This study introduces a deep learning approach to improve teledermatology for skin lesion diagnosis. The new pipeline enhances risk prioritization by combining AI-driven diagnosis with expert knowledge, improving early detection accuracy.

Keywords:

curriculum learning domain knowledge hierarchical learning risk prioritization skin lesion classification teledermatology

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Area of Science:

Dermatology
Artificial Intelligence
Medical Informatics

Background:

Teledermatology is crucial for early skin lesion diagnosis.
Existing teledermatology processes require optimization for risk prioritization.

Purpose of the Study:

To develop a deep learning approach for improved risk prioritization in teledermatology.
To integrate domain knowledge into an automated prioritization pipeline.

Main Methods:

Utilized a retrospective dataset from the Portuguese National Health System.
Explored automatic lesion segmentation and curriculum learning strategies.
Combined predicted diagnoses with expert-derived priority levels.

Main Results:

Lesion segmentation improved classification accuracy compared to baseline models.
Curriculum learning outperformed hierarchical and flat approaches.
The combined approach achieved a macro F1 score of 43.93% on a validated test set.

Conclusions:

The proposed knowledge-driven deep learning pipeline shows promise for enhancing teledermatology risk prioritization.
Data-centric and knowledge-driven approaches are valuable for advancing teledermatology.
Further research can refine these methods for clinical application.