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

You might also read

Related Articles

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

Sort by

Same author

The central role of radiotherapy in remodeling the tumor immune microenvironment: mechanisms and therapeutic implications.

Frontiers in cell and developmental biology·2026

Same author

Diagnostic value of cystatin C in acute kidney injury among patients with sepsis: a systematic review and meta-analysis.

Frontiers in medicine·2026

Same author

Characterization of oxidative status in maize protoplasts under temperature and saline-alkali stresses.

BMC plant biology·2026

Same author

RNA interference targeting BxNDUFA2 impairs mitochondrial function and triggers oxidative stress to control pine wood nematode (Bursaphelenchus xylophilus).

Pesticide biochemistry and physiology·2026

Same author

Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversation.

Proceedings of the conference. Association for Computational Linguistics. European Chapter. Conference·2026

Same author

Correction: Comprehensive immune profiling identifies alterations in adaptive and innate immune responses in granulomatosis with polyangiitis patients in remission.

Frontiers in immunology·2026

Same journal

Sensitivity Analyses of a Scoring System for a Contraception Decision Aid.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

Improving electronic health record processing of large language models via retrieval-augmented generation: A case study on dietary supplements.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

Developing a User-Centered Mobile Application Prototype: Bridging Lower-Limb Fracture Care from Skilled Nursing Facility and Back to the Community.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

Automating Adjudication of Cardiovascular Events Using Large Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same journal

Predictive Factors and State-Level Barriers to Postpartum Birth Control Usage in the United States: Insights from PRAMS Phase 8.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 5, 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

Local Contrastive Learning for Medical Image Recognition.

Syed A Rizvi¹, Ruixiang Tang², Xiaoqian Jiang³

¹Yale University, New Haven, CT.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|January 15, 2024

Summary

This summary is machine-generated.

Local Region Contrastive Learning (LRCLR) improves medical image analysis by identifying key regions and linking them to radiology reports. This deep learning approach enhances diagnostic accuracy without needing expert labels.

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Related Experiment Videos

Last Updated: Jul 5, 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

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Deep Learning (DL) methods are increasingly used for radiographic image analysis.
Expert-labeled radiology data is in high demand.
Self-supervised frameworks use radiology reports but struggle with subtle pathology differences and lack region-text interpretability.

Purpose of the Study:

To introduce Local Region Contrastive Learning (LRCLR), a novel fine-tuning framework.
To enable DL models to identify significant image regions and their relation to text.
To improve the interpretability and performance of DL models in medical image analysis.

Main Methods:

Developed LRCLR, a flexible fine-tuning framework.
Integrated layers for significant image region selection.
Incorporated cross-modality interaction for image-text correlation.

Main Results:

LRCLR effectively identifies significant local regions in chest X-rays.
The framework provides meaningful interpretation between image regions and radiology text.
Demonstrated improved zero-shot performance on multiple chest X-ray medical findings.

Conclusions:

LRCLR offers a flexible and interpretable approach to medical image analysis.
The method enhances the utility of self-supervised learning by bridging image and text data.
LRCLR shows promise for improving diagnostic capabilities in radiology.