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Updated: Sep 9, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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CAT: Class-aware adaptive-thresholding for robust semi-supervised domain generalization.

Sumaiya Zoha1, Jeong-Gun Lee2, Young-Woong Ko2

  • 1Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh.

Plos One
|September 4, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces CAT, a novel semi-supervised domain generalization method using adaptive thresholding and pseudo-label refinement. It achieves strong generalization performance with limited labeled data, overcoming challenges of domain shifts.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Domain Generalization (DG) aims to transfer knowledge across domains, but requires extensive labeled data.
  • High-quality labeled data is costly and labor-intensive, limiting practical DG applications.
  • Semi-supervised Domain Generalization (SSDG) offers a label-efficient alternative.

Purpose of the Study:

  • Investigate a practical SSDG problem under a label-efficient paradigm.
  • Propose a novel method, CAT, for competitive generalization performance with limited labeled data.
  • Address limitations of previous methods, including fixed thresholds and noisy pseudo-labels.

Main Methods:

  • Leverage semi-supervised learning with limited labeled data.
  • Employ adaptive thresholding for high-quality pseudo-label generation with class diversity.
  • Utilize noisy label refinement techniques to enhance pseudo-label reliability.

Main Results:

  • CAT achieves competitive generalization performance under domain shifts.
  • Demonstrated superior performance on benchmark datasets: PACS (+3.45%), OfficeHome (+9.47%), and miniDomainNet (+10.90%).
  • Highlights effectiveness in achieving robust generalization despite domain shifts.

Conclusions:

  • CAT provides a straightforward yet highly effective solution for SSDG tasks.
  • The method successfully overcomes reliance on fixed thresholds and sensitivity to noisy pseudo-labels.
  • Achieves robust generalization in label-efficient settings, enhancing practical DG applicability.