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Updated: Oct 9, 2025

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An Adaptive Low-Rank Modeling-Based Active Learning Method for Medical Image Annotation.

S He1, J Wu2, C Lian3

  • 1Department of Computer Science, Washington University, St. Louis, MO, USA.

Ingenierie Et Recherche Biomedicale : IRBM = Biomedical Engineering and Research
|December 22, 2021
PubMed
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This summary is machine-generated.

This study introduces a novel low-rank modeling-based multi-label active learning (LRMMAL) method for medical image analysis. It effectively selects informative examples to train classifiers, overcoming challenges like image noise and large datasets.

Area of Science:

  • Medical image analysis
  • Machine learning
  • Computer vision

Background:

  • Active learning efficiently trains models with limited annotated data, crucial for medical imaging where unlabeled data is abundant but annotation is costly.
  • Challenges in medical active learning include image noise, large datasets, and diverse imaging modalities, complicating informative example selection.

Purpose of the Study:

  • To develop a novel low-rank modeling-based multi-label active learning (LRMMAL) method for optimal medical image classification.
  • To address challenges in medical image active learning, including noise, data volume, and modality variety.

Main Methods:

  • Developed a low-rank modeling-based multi-label active learning (LRMMAL) method.
  • Quantified image noise independently and integrated it into a pool-based sampling process for informative example selection.

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  • Proposed an automatic adaptive cross-entropy-based parameter determination scheme to optimize sampling.

    • Main Results:

    • The LRMMAL method demonstrated superior performance in selecting informative examples for training classifiers.
    • Experimental results on varied medical image datasets confirmed the effectiveness of the proposed method.
    • Comparisons with state-of-the-art multi-label active learning methods showed significant improvements.

    Conclusions:

    • The proposed LRMMAL method effectively addresses key challenges in medical image active learning.
    • This approach enhances classifier performance by optimizing the selection of informative training examples.
    • The method offers a promising solution for efficient and accurate medical image analysis.