Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Elution kinetics and antimicrobial efficacy of high-dose gram-negative antibiotic-loaded acrylic cement: an in vitro and in vivo study.

BMC microbiology·2026

Same author

Comparative analysis of whole spine and sacroiliac joint magnetic resonance SPARCC scores in patients with SAPHO syndrome and ankylosing spondylitis.

Frontiers in immunology·2026

Same author

An interpretable multimodal fusion model for improving diagnosis and prognosis prediction of kidney allograft rejection.

Kidney research and clinical practice·2026

Same author

Predicting major organ complications in primary Sjögren's disease using a machine learning ensemble strategy: a dual-center retrospective clinical study.

Frontiers in immunology·2026

Same author

Layer Codes as Partially Self-Correcting Quantum Memories.

Physical review letters·2026

Same author

CLIS: Causality-inspired Longitudinal Image Synthesis and its application to Alzheimer's disease characterization.

Medical image analysis·2026

Same journal

Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.

Medical image analysis·2026

Same journal

MBAS2024: A large-scale benchmark for multi-class bi-atrial segmentation in multi-center contrast-enhanced MRIs.

Medical image analysis·2026

Same journal

Respiratory motion augmentation for personalized super-resolution (RMApSR) of 3D cine MR images in MRI-guided radiotherapy.

Medical image analysis·2026

Same journal

Biom3d, a modular framework to host and develop 3D segmentation methods.

Medical image analysis·2026

Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026

Same journal

Real-time patient-specific microwave ablation zone prediction via a unified bioheat solver and MRI-informed perturbation learning.

Medical image analysis·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 17, 2025

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

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

Published on: November 30, 2022

ECAMP: Entity-centered Context-aware Medical Vision Language Pre-training.

Rongsheng Wang¹, Qingsong Yao², Zihang Jiang³

¹School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC), Hefei Anhui, 230026, China; Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advance Research, USTC, Suzhou Jiangsu, 215123, China; Anhui IFLYTEK CO., Ltd., China.

Medical Image Analysis

|July 2, 2025

Summary

This summary is machine-generated.

The new Entity-centered Context-aware Medical Vision-language Pre-training (ECAMP) framework improves medical image analysis by better understanding complex reports. ECAMP enhances vision encoders for more accurate medical AI tasks.

Keywords:

Cross-modality Learning Masked Modeling Medical Vision-language Pre-training

More Related Videos

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

Related Experiment Videos

Last Updated: Sep 17, 2025

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

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

Published on: November 30, 2022

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

Area of Science:

Artificial Intelligence
Medical Imaging
Natural Language Processing

Background:

Current medical vision-language pre-training methods neglect linguistic complexity and data imbalance in reports.
Existing models struggle with intricate cross-modality relationships between medical text and images.

Purpose of the Study:

To introduce the Entity-centered Context-aware Medical Vision-language Pre-training (ECAMP) framework.
To enhance the pre-training of vision encoders using a more entity-centered, context-sensitive, and balanced approach to medical reports.

Main Methods:

Distilling entity-centered context from medical reports using large language models for precise text supervision.
Incorporating entity-aware re-balanced factors and descriptor masking in masked language modeling to improve entity knowledge.
Utilizing a context-guided super-resolution task and multi-scale context fusion for better integration of image representations.

Main Results:

ECAMP achieves significant performance improvements over state-of-the-art methods in medical vision-language pre-training.
Demonstrated cutting-edge results on classification, segmentation, and detection tasks.
Validated effectiveness across diverse domains and organs using chest X-ray and fundoscopy datasets.

Conclusions:

ECAMP establishes a new standard for cross-modality pre-training in medical imaging.
The framework effectively addresses limitations in existing medical report analysis for AI.
ECAMP shows strong potential for advancing AI-driven medical diagnosis and analysis.