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Related Experiment Video

Updated: Jun 13, 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

Language-assisted multimodal convolutional transformer pipeline for retinal lesions segmentation.

Wilayat Khan1, Mohammad Alsaffar2, Muhammad Faisal Abrar3

  • 1Department of Computer Engineering, University of Ha'il, Ha'il, Saudi Arabia.

Scientific Reports
|June 11, 2026
PubMed
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This study introduces a novel language-assisted AI model for retinal lesion segmentation. The model aligns image and text features, improving accuracy without needing pixel-level data.

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Retinal lesion segmentation is crucial for diagnosing eye diseases.
  • Current deep learning models often lack clinical relevance and require extensive pixel-level annotations.
  • These limitations hinder accurate and efficient retinal disease analysis.

Purpose of the Study:

  • To develop a novel language-assisted multimodal convolutional transformer pipeline for retinal lesion segmentation.
  • To overcome the limitations of existing models by aligning image and text features.
  • To enable robust lesion extraction without pixel-level ground truth annotations.

Main Methods:

  • A multimodal convolutional transformer pipeline was designed to align retinal scan image features with text features from clinical prompts.
Keywords:
Deep learningOptical coherence tomographyRetinal lesions segmentationVision language models

Related Experiment Videos

Last Updated: Jun 13, 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

  • A novel loss function was employed for one-time training to establish feature alignment.
  • The model was trained to infer learning from text prompts, eliminating the need for dataset-specific pixel-level annotations.

    • Main Results:

    • The proposed network demonstrated robust retinal lesion extraction across six public datasets.
    • The model achieved up to 7.77% improvement in intersection-over-union compared to state-of-the-art methods.
    • The language-assisted approach proved effective in adapting to new datasets without retraining.

    Conclusions:

    • The developed language-assisted multimodal pipeline offers a significant advancement in retinal lesion segmentation.
    • This approach enhances clinical relevance and reduces the dependency on laborious pixel-level annotations.
    • The model shows strong potential for improving the diagnosis and management of retinal diseases.