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Semantically redundant training data removal and deep model classification performance: A study with chest X-rays.

Sivaramakrishnan Rajaraman1, Ghada Zamzmi1, Feng Yang1

  • 1National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|April 12, 2024
PubMed
Summary
This summary is machine-generated.

Removing semantically redundant data improves deep learning (DL) model performance in medical imaging. An entropy-based approach identified informative samples, leading to significantly better recall in chest X-ray classification.

Keywords:
Chest X-raysDeep learningInformative sample selectionSemantic redundancyStatistical significance

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging Analysis
  • Machine Learning

Background:

  • Deep learning (DL) models excel at learning from complex data, with performance often linked to training data volume.
  • Medical imaging datasets can contain semantic redundancy, where similar images reduce model variety and potentially limit generalization.
  • Standard data augmentation techniques may not overcome redundancy and can even hinder DL performance if applied indiscriminately.

Purpose of the Study:

  • To investigate the impact of semantic redundancy in training data on deep learning classifier performance for medical imaging.
  • To develop and evaluate an entropy-based method for identifying and removing semantically redundant samples from training datasets.
  • To demonstrate improved model generalizability and performance by training on an informative subset of data.

Main Methods:

  • Proposed an entropy-based sample scoring method to quantify and identify semantically redundant data points.
  • Applied the method to the NIH chest X-ray dataset to curate a subset of informative training samples.
  • Trained and evaluated deep learning classifiers on both the full dataset and the curated informative subset.

Main Results:

  • The model trained on the informative subset significantly outperformed the model trained on the full dataset in internal testing (recall: 0.7164 vs 0.6597, p<0.05).
  • External testing also showed superior performance for the model trained on the informative subset (recall: 0.3185 vs 0.2589, p<0.05).
  • Results indicate that semantic redundancy negatively impacts classifier performance and generalizability.

Conclusions:

  • Semantic redundancy in training data can significantly degrade deep learning model performance and limit generalizability.
  • Information-oriented sample selection, rather than using all available data, is crucial for effective deep learning in medical imaging.
  • The proposed entropy-based approach offers a viable strategy for curating high-quality training datasets to enhance model performance.