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Related Experiment Video

Updated: Jun 4, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

IQE-CLIP: Instance-Aware Query Embedding for Zero-/Few-Shot Anomaly Detection in Medical Domain.

Hong Huang, Weixiang Sun, Zhijian Wu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 2, 2026
    PubMed
    Summary
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    This study introduces IQE-CLIP, a novel framework for medical anomaly detection using vision-language models. IQE-CLIP enhances zero-shot and few-shot anomaly detection by generating query embeddings sensitive to abnormalities.

    Area of Science:

    • Computer Vision
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Vision-language models like CLIP have advanced zero-/few-shot anomaly detection (ZFSAD).
    • Existing CLIP-based ZFSAD methods often require category knowledge and specific prompts, limiting their ability to distinguish anomalies in joint embedding spaces.
    • Current ZFSAD research primarily focuses on industrial applications, with limited exploration in the medical domain.

    Purpose of the Study:

    • To propose an innovative framework, IQE-CLIP, for ZFSAD tasks specifically tailored for the medical domain.
    • To enhance the sensitivity of query embeddings to abnormalities by incorporating both textual and instance-aware visual information.
    • To improve the adaptation of CLIP for medical ZFSAD tasks.

    Main Methods:

    Related Experiment Videos

    Last Updated: Jun 4, 2026

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

  • Developed IQE-CLIP, a framework for medical ZFSAD.
  • Introduced class-based and learnable prompting tokens to adapt CLIP for the medical domain.
  • Designed an instance-aware query module (IQM) to extract region-level contextual information, generating anomaly-sensitive query embeddings.

    • Main Results:

    • IQE-CLIP achieved state-of-the-art performance on six medical datasets for both zero-shot and few-shot ZFSAD tasks.
    • The proposed query embeddings, integrating textual and instance-aware visual data, proved effective in identifying abnormalities.
    • The IQM successfully enhanced the sensitivity of embeddings to anomalies.

    Conclusions:

    • IQE-CLIP offers a significant advancement in medical ZFSAD.
    • The framework demonstrates the efficacy of instance-aware query embeddings for anomaly detection in medical imaging.
    • IQE-CLIP provides a robust solution for zero-shot and few-shot medical anomaly detection challenges.