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Updated: Jan 9, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

991

Improving Clinical Generalization of Pressure Ulcer Stage Classification Through Saliency-Guided Data Augmentation.

Jun-Woo Choi1, Won Lo Rhee2, Dong-Hun Han1

  • 1Department of Medical Artificial Intelligence, Eulji University, Seongnam 13135, Republic of Korea.

Diagnostics (Basel, Switzerland)
|December 11, 2025
PubMed
Summary

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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This study enhances pressure ulcer staging accuracy using a novel two-phase training approach. Clinically informed data augmentation significantly improves model generalization for medical imaging applications.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Dermatology

Background:

  • Medical imaging datasets for specific conditions like pressure ulcers are often limited.
  • Variations in imaging conditions (distance, lighting, viewpoint) hinder accurate clinical classification.
  • Developing robust AI models for pressure ulcer staging is crucial for effective patient care.

Purpose of the Study:

  • To improve the generalization capability of AI models for pressure ulcer stage classification.
  • To address challenges posed by data scarcity and variability in clinical medical imaging.
  • To enhance the clinical usability of AI-powered diagnostic tools.

Main Methods:

  • Developed a YOLOv7-based model for pressure ulcer stage classification.
Keywords:
YOLOv7classificationcurriculum learningdata augmentationdomain shiftgeneralizationpressure ulcersaliency map

Related Experiment Videos

Last Updated: Jan 9, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

991
  • Employed a two-phase training strategy incorporating saliency-guided images.
  • Utilized clinically plausible noise augmentation, including healing areas and white keratin.

    • Main Results:

    • Achieved an accuracy increase from 75% to 89% on newly acquired hospital images.
    • Demonstrated stable and reproducible performance with five-fold cross-validation (mAP@0.5: 86.20% ± 2.28%).
    • Exceeded prior reported performance benchmarks for pressure ulcer staging models in clinical settings.

    Conclusions:

    • Curriculum learning combined with noise-enriched augmentation improves model generalization in clinical settings.
    • Clinically informed data augmentation is essential for enhancing AI model performance in medical imaging.
    • The proposed approach offers a practical solution for improving AI usability in data-limited medical environments.