GazeLT classifies long-tailed chest X-ray diseases by training a student model with a teacher that learns time-windowed radiologist gaze attention, improving tail-class accuracy on the NIH-CXR-LT and MIMIC-CXR-LT benchmarks.
GazeSAM: What You See is What You Segment
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abstract
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 \textbf{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 \url{https://github.com/ukaukaaaa/GazeSAM}.
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GazeLT: Visual attention-guided long-tailed disease classification in chest radiographs
GazeLT classifies long-tailed chest X-ray diseases by training a student model with a teacher that learns time-windowed radiologist gaze attention, improving tail-class accuracy on the NIH-CXR-LT and MIMIC-CXR-LT benchmarks.