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Related Concept Videos

Visual System01:26

Visual System

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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From Gaze to Insight: Bridging Human Visual Attention and Vision Language Model Explanation for Weakly-Supervised

Jingkun Chen, Haoran Duan, Xiao Zhang

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    |October 6, 2025
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    Summary
    This summary is machine-generated.

    This study introduces a novel teacher-student framework for medical image segmentation, effectively combining clinician gaze data and vision-language models (VLMs). The approach enhances segmentation accuracy without increasing annotation costs, improving diagnostic AI systems.

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    Area of Science:

    • Medical Image Analysis
    • Artificial Intelligence in Medicine
    • Computer Vision

    Background:

    • Medical image segmentation is crucial but hindered by high annotation costs.
    • Existing weak supervision methods like clinician gaze data are sparse, and vision-language models (VLMs) lack precision.
    • Neither gaze nor language supervision alone is sufficient for accurate segmentation.

    Purpose of the Study:

    • To develop an annotation-efficient medical image segmentation method by integrating clinician gaze and VLM supervision.
    • To leverage the complementary strengths of gaze data (where) and VLM (why) for improved segmentation.
    • To enhance the interpretability and deployability of AI systems in medical diagnostics.

    Main Methods:

    • A teacher-student framework was proposed, where a teacher model learns from gaze points enhanced by VLM-generated lesion descriptions.
    • The teacher guides the student via multi-scale feature alignment, confidence-weighted consistency constraints, and adaptive masking.
    • The framework integrates human visual attention with AI-generated semantic context.

    Main Results:

    • The proposed method achieved high Dice scores: 80.78% (Kvasir-SEG), 80.53% (NCI-ISBI), and 84.22% (ISIC).
    • Performance improved by 3-5% over gaze-only baselines without additional annotation burden.
    • The framework maintained clinical interpretability by preserving correlations between predictions, gaze, and lesion descriptions.

    Conclusions:

    • Integrating human gaze and VLM semantic context effectively overcomes limitations of individual weak supervision signals.
    • The developed framework offers an annotation-efficient approach for medical AI, advancing deployable segmentation systems.
    • This work highlights the potential of combining human attention and AI for robust medical image analysis.