ArticleScientific data2022
REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 47 citations in OpenAlex.
- Directional dynamics reveal regimes of human visual examination.Scientific reports · 2026Article
- Eye tracking as a tool to quantify the effects of CAD display on radiologists' interpretation of chest radiographs.European journal of radiology open · 2026Article
- A Two-Stage Contrastive Learning Framework Grounded in Label-Specific Features for Low-Frequency Labels in Chest X-Ray Multi-Label Classification.Bioengineering (Basel, Switzerland) · 2026Article
- [Visual prior-guided masked image modeling enhances chest X-ray diagnostic efficacy].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Article
- RadGazeGen: radiomics and gaze-guided chest X-ray generation using diffusion models.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Eye Tracking-Enhanced Deep Learning for Medical Image Analysis: A Systematic Review on Data Efficiency, Interpretability, and Multimodal Integration.Bioengineering (Basel, Switzerland) · 2025Review
- Collaborative Integration of AI and Human Expertise to Improve Detection of Chest Radiograph Abnormalities.Radiology. Artificial intelligence · 2025Article
- Eye-Guided Multimodal Fusion: Toward an Adaptive Learning Framework Using Explainable Artificial Intelligence.Sensors (Basel, Switzerland) · 2025Article
- Modeling radiologists' cognitive processes using a digital gaze twin to enhance radiology training.Scientific reports · 2025Article
- ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists' intentions.Artificial intelligence in medicine · 2025Article
- Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.JMIR formative research · 2025Article
- Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: A data-driven approach for improved classification.Medical image analysis · 2025Article
- Bridging human and machine intelligence: Reverse-engineering radiologist intentions for clinical trust and adoption.Computational and structural biotechnology journal · 2024Article
- The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review.IEEE journal of biomedical and health informatics · 2024Review
- DECODING RADIOLOGISTS' INTENTIONS: A NOVEL SYSTEM FOR ACCURATE REGION IDENTIFICATION IN CHEST X-RAY IMAGE ANALYSIS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Vision-language models for medical report generation and visual question answering: a review.Frontiers in artificial intelligence · 2024Review
- MDF-Net for abnormality detection by fusing X-rays with clinical data.Scientific reports · 2023Article
- Localization supervision of chest x-ray classifiers using label-specific eye-tracking annotation.Frontiers in radiology · 2023Article
- REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays.Scientific data · 2022Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Deep learning has shown recent success in classifying anomalies in chest x-rays, but datasets are still small compared to natural image datasets. Supervision of abnormality localization has been shown to improve trained models, partially compensating for dataset sizes. However, explicitly labeling these anomalies requires an expert and is very time-consuming. We propose a potentially scalable method for collecting implicit localization data using an eye tracker to capture gaze locations and a microphone to capture a dictation of a report, imitating the setup of a reading room. The resulting REFLACX (Reports and Eye-Tracking Data for Localization of Abnormalities in Chest X-rays) dataset was labeled across five radiologists and contains 3,032 synchronized sets of eye-tracking data and timestamped report transcriptions for 2,616 chest x-rays from the MIMIC-CXR dataset. We also provide auxiliary annotations, including bounding boxes around lungs and heart and validation labels consisting of ellipses localizing abnormalities and image-level labels. Furthermore, a small subset of the data contains readings from all radiologists, allowing for the calculation of inter-rater scores.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.