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A Comparison of Object Detection and Phrase Grounding Models in Chest X-ray Abnormality Localization using Eye-tracking Data

arXiv 2503.01037 ↗First posted 2 Mar 2025cs.CV cs.LGcited by 0

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Abstract (arXiv)

Chest diseases rank among the most prevalent and dangerous global health issues. Object detection and phrase grounding deep learning models interpret complex radiology data to assist healthcare professionals in diagnosis. Object detection locates abnormalities for classes, while phrase grounding locates abnormalities for textual descriptions. This paper investigates how text enhances abnormality localization in chest X-rays by comparing the performance and explainability of these two tasks. To establish an explainability baseline, we proposed an automatic pipeline to generate image regions for report sentences using radiologists' eye-tracking data. The better performance - mIoU = 0.36 vs. 0.20 - and explainability - Containment ratio 0.48 vs. 0.26 - of the phrase grounding model infers the effectiveness of text in enhancing chest X-ray abnormality localization.

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References · 9

  1. 2019Publicly Available Clinical BERT Embeddingscited by 995
  2. 2020Contrastive Learning of Medical Visual Representations from Paired Images and Textcited by 636
  3. 2019MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographscited by 590
  4. 2023Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Imagescited by 128
  5. 2021REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rayscited by 34
  6. 2023Medical Phrase Grounding with Region-Phrase Context Contrastive Alignmentcited by 32
  7. 2023GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classificationcited by 25
  8. 2024Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patternscited by 8
  9. 2022Localization supervision of chest x-ray classifiers using label-specific eye-tracking annotationcited by 3

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