mlsanityData to 14 Oct 2026

GazeSAM: What You See is What You Segment

arXiv 2304.13844 ↗First posted 26 Apr 2023cs.CVcited by 19

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Abstract (arXiv)

This study investigates the potential of eye-tracking technology and the Segment Anything Model (SAM) to design a collaborative human-computer interaction system that automates medical image segmentation. We present the GazeSAM system to enable radiologists to collect segmentation masks by simply looking at the region of interest during image diagnosis. The proposed system tracks radiologists' eye movement and utilizes the eye-gaze data as the input prompt for SAM, which automatically generates the segmentation mask in real time. This study is the first work to leverage the power of eye-tracking technology and SAM to enhance the efficiency of daily clinical practice. Moreover, eye-gaze data coupled with image and corresponding segmentation labels can be easily recorded for further advanced eye-tracking research. The code is available in urlhttps://github.com/ukaukaaaa/GazeSAM.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 2 of its 10 references that are in the map. First posted on arXiv 26 Apr 2023.

Builds on

2 of 10 references.

References · 2

  1. 2023Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imagingcited by 161
  2. 2018A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learningcited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 7; 2024: 7; 2025: 4; 2026: 1.

19 papers.

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  1. 2024Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directionscited by 97
  2. 2023A Comprehensive Survey on Segment Anything Model for Vision and Beyondcited by 64
  3. 2023A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineeringcited by 45
  4. 2024Foundation Models for Biomedical Image Segmentation: A Surveycited by 28
  5. 2023Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomycited by 27
  6. 2023GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classificationcited by 25
  7. 2023Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Surveycited by 21
  8. 2024A Survey on Trustworthiness in Foundation Models for Medical Image Analysiscited by 17
  9. 2024Gazing Into Missteps: Leveraging Eye-Gaze for Unsupervised Mistake Detection in Egocentric Videos of Skilled Human Activitiescited by 15
  10. 2023Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)cited by 7
  11. 2025Integrating Travel Behavior Forecasting and Generative Modeling for Predicting Future Urban Mobility and Spatial Transformationscited by 5
  12. 2023GlanceSeg: Real-time microaneurysm lesion segmentation with gaze-map-guided foundation model for early detection of diabetic retinopathycited by 4
7 more
  1. 2024RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Modelscited by 4
  2. 2024Gaze-DETR: Using Expert Gaze to Reduce False Positives in Vulvovaginal Candidiasis Screeningcited by 3
  3. 2025Zero-Shot Gaze-based Volumetric Medical Image Segmentationcited by 2
  4. 2024Vision-language models for decoding provider attention during neonatal resuscitationcited by 1
  5. 2025GazeLT: Visual attention-guided long-tailed disease classification in chest radiographscited by 1
  6. 2025Eye on the Target: Eye Tracking Meets Rodent Trackingcited by 0
  7. 2026GazeRefine: Expert Gaze as a Test-Time Prompt for Training-Free Medical Image Segmentationcited by 0