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Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population.

Mayanka Chandrashekar1, Ian Goethert2, Md Inzamam Ul Haque3

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Summary
This summary is machine-generated.

Domain shift significantly impacts chest X-ray classification accuracy, with study year being a key factor. Addressing this is crucial for robust medical imaging models and improved patient care.

Keywords:
Chest X-ray image classificationDomain shiftMulti-label classification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiology Informatics

Background:

  • Deep learning models for chest X-ray classification are sensitive to domain shift, affecting performance.
  • Ground truth label quality and demographic factors can influence classification accuracy.
  • Existing chest X-ray datasets may exhibit variations due to data collection and labeling methods.

Purpose of the Study:

  • To assess the impact of domain shift on chest X-ray classification accuracy.
  • To analyze the influence of ground truth label quality and demographic factors (age, sex, study year) on classification performance.
  • To compare multi-label classification performance across different datasets and natural language processing (NLP) extraction tools.

Main Methods:

  • Utilized a DenseNet121 model pre-trained on the MIMIC-CXR dataset for multi-label classification.
  • Extracted ground truth labels from radiology reports using CheXpert and CheXbert Labeler.
  • Compared model performance on the MIMIC-CXR and Veterans Healthcare Administration chest X-ray (VA-CXR) datasets.

Main Results:

  • The VA-CXR dataset showed lower ground truth disagreement rates compared to MIMIC-CXR.
  • Minimal domain shift was observed in the VA dataset, except for the 'Enlarged Cardiomediastinum' label.
  • Study year emerged as the subgroup with the most significant variations in multi-label classification performance.

Conclusions:

  • Domain shift and demographic factors, particularly the temporality of exams (study year), significantly impact chest X-ray classification.
  • Findings highlight the need for improved transfer learning strategies and robust model development in medical imaging AI.
  • Addressing domain shift is essential for advancing AI in radiology and enhancing patient care.