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Related Experiment Video

Updated: Oct 19, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Incremental Learning for Dermatological Imaging Modality Classification.

Ana C Morgado1,2, Catarina Andrade1, Luís F Teixeira2,3

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

Journal of Imaging
|September 26, 2021
PubMed
Summary
This summary is machine-generated.

This study enhances teledermatology by classifying dermatological image modalities using AI. MobileNetV2 with experience replay achieved high accuracy while minimizing knowledge loss in new conditions.

Keywords:
catastrophic forgettingcontinual learningmodality classificationteledermatology

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Area of Science:

  • Artificial Intelligence in Medicine
  • Teledermatology Image Analysis
  • Machine Learning for Healthcare

Background:

  • Teledermatology adoption necessitates improved medical record organization.
  • Dermatological image modality classification is crucial for filtering records.
  • Existing medical imaging modality classification lacks dermatology-specific focus.

Purpose of the Study:

  • To classify dermatological images by modality using deep learning models.
  • To evaluate incremental learning strategies for adapting models to changing acquisition conditions.
  • To assess models based on accuracy, catastrophic forgetting, and computational cost.

Main Methods:

  • Utilized VGG-16 and MobileNetV2 models for image classification.
  • Applied four incremental learning strategies: naive, EWC, AGEM, and experience replay.
  • Evaluated performance considering accuracy, forgetting, and resource usage.

Main Results:

  • MobileNetV2 with experience replay achieved 86.04% accuracy.
  • This strategy demonstrated minimal forgetting (0.0344), outperforming others by 6.98%.
  • The approach incurred additional training time (56s/epoch) and RAM (4554MB).

Conclusions:

  • The proposed approach effectively classifies dermatological image modalities.
  • Incremental learning, particularly experience replay, enables model adaptation without significant knowledge loss.
  • This method shows promise for organizing teledermatology records.