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

Radiological Investigation I: X-ray and CT

Radiological Investigation I: X-ray and CT

Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...

You might also read

Related Articles

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

Sort by

Same author

Simultaneous Improvement of Bendability and Passive Daytime Radiative Cooling Performance in Multilayer Alumina Fiber Membranes.

Materials (Basel, Switzerland)·2026

Same author

Decoding amino acid metabolism in hepatocellular carcinoma: critical nodes and novel therapeutic strategy.

Frontiers in oncology·2026

Same author

The in vivo inhibitory function of the MHC-I α3 domain-CD8α interaction.

bioRxiv : the preprint server for biology·2026

Same author

SARS-CoV-2 ORF3a suppresses host antiviral interferon responses by promoting STUB1-mediated PTEN proteasomal degradation.

Journal of virology·2026

Same author

The mediating effect of meaning in life on fear of death and attitudes toward caring for the dying among oncology nurses.

BMC nursing·2026

Same author

Chemical derivatization of citrullinated peptides using methylglyoxal and sodium 3-mercaptopropanesulfonate (MG/MPS).

Analytical methods : advancing methods and applications·2026

Search research articles

Related Experiment Video

Updated: May 24, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Learning Consistent Semantic Representation for Chest X-ray via Anatomical Localization in Self-Supervised

Surong Chu, Xueting Ren, Guohua Ji

IEEE Journal of Biomedical and Health Informatics

|March 3, 2025

Summary

This study introduces anatomy-aware representation learning (AARL) for chest X-ray (CXR) images. AARL enhances self-supervised learning by using anatomical positions to create consistent image representations, improving diagnostic accuracy.

More Related Videos

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

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Related Experiment Videos

Last Updated: May 24, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Chest X-ray (CXR) images present anatomical variations despite similar global structures.
Learning consistent anatomical representations from diverse CXR appearances is challenging for self-supervised pre-training.

Purpose of the Study:

To develop a novel self-supervised pre-training framework for CXR images that addresses appearance diversity.
To improve the learning of consistent anatomical semantic representations.

Main Methods:

Proposed two new pre-training tasks: inner-image anatomy localization (IIAL) and cross-image anatomy localization (CIAL).
Introduced a unified end-to-end framework, anatomy-aware representation learning (AARL), integrating IIAL, CIAL, and pixel restoration.
Utilized position information as supervision for learning semantic representations.

Main Results:

AARL demonstrated superior representation and transfer learning abilities across six downstream tasks (classification, segmentation).
The framework proved annotation-efficient, reducing the need for labeled data.
Improved sensitivity in detecting various pathological and anatomical patterns.

Conclusions:

AARL effectively learns consistent anatomical representations from diverse CXR images.
The proposed method enhances diagnostic performance and reduces data annotation requirements.
AARL offers a powerful and efficient approach for self-supervised learning in medical imaging.