Integrating Eye-Gaze Data into CXR DL Approaches: A Preliminary study
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Abstract (arXiv)
This paper proposes a novel multimodal DL architecture incorporating medical images and eye-tracking data for abnormality detection in chest x-rays. Our results show that applying eye gaze data directly into DL architectures does not show superior predictive performance in abnormality detection chest X-rays. These results support other works in the literature and suggest that human-generated data, such as eye gaze, needs a more thorough investigation before being applied to DL architectures.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 36 references that are in the map. First posted on arXiv 6 Feb 2023.
Builds on
15 of 36 references.
References · 15
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
- 2015Fast R-CNNcited by 2,933
- 2018CBAM: Convolutional Block Attention Modulecited by 2,477
- 2019MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographscited by 597
- 2021Deep Learning for Chest X-ray Analysis: A Surveycited by 84
- 2018A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizationscited by 60
- 2019Visualizing Deep Networks by Optimizing with Integrated Gradientscited by 59
- 2020Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Developmentcited by 56
- 2022Follow My Eye: Using Gaze to Supervise Computer-Aided Diagnosiscited by 38
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.