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

Imaging Studies I: Kidney, Ureter, and Bladder Studies

Imaging Studies I: Kidney, Ureter, and Bladder Studies

Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...

You might also read

Related Articles

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

Sort by

Same author

SGRRG: Leveraging radiology scene graphs for improved and abnormality-aware radiology report generation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2025

Same author

Characterization of Breast Cancer Intra-Tumor Heterogeneity Using Artificial Intelligence.

Cancers·2024

Same author

CAMANet: Class Activation Map Guided Attention Network for Radiology Report Generation.

IEEE journal of biomedical and health informatics·2024

Same author

Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc·2023

Same author

AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer.

NPJ precision oncology·2023

Same author

Development and validation of artificial intelligence-based prescreening of large-bowel biopsies taken in the UK and Portugal: a retrospective cohort study.

The Lancet. Digital health·2023

Same journal

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

IEEE journal of biomedical and health informatics·2026

Same journal

NeuroBooster: a domain-informed self-supervised learning paradigm tailored for brain MRI analysis.

IEEE journal of biomedical and health informatics·2026

Same journal

Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures.

IEEE journal of biomedical and health informatics·2026

Same journal

Improving Multi-Sensor Non-Invasive Glucose Detection through AI: A Domain Generalization Approach.

IEEE journal of biomedical and health informatics·2026

Same journal

Unmixing the Neck: Accurate Jugular Venous Pulse Detection From Wearable PPG.

IEEE journal of biomedical and health informatics·2026

Same journal

AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders.

IEEE journal of biomedical and health informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 6, 2026

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

Improving Medical Visual Representation Learning With Pathological-Level Cross-Modal Alignment and Correlation

Jun Wang, Lixing Zhu, Xiaohan Yu

IEEE Journal of Biomedical and Health Informatics

|October 23, 2025

Summary

This summary is machine-generated.

This study introduces PLACE, a framework for medical image and report analysis. It enhances understanding of pathology by aligning visual and textual data at a detailed level, improving performance on various medical tasks.

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

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Jun 6, 2026

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

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Area of Science:

Medical imaging analysis
Natural Language Processing
Machine Learning

Background:

Joint learning from medical image-report pairs is crucial for transferring knowledge to downstream tasks.
Prior methods often focus on instance or token-level alignment, overlooking pathology-level consistency.
Developing methods for robust medical visual representation learning is an active research area.

Purpose of the Study:

To present a novel framework, PLACE, for pathological-level alignment and fine-grained detail enrichment in medical image-report learning.
To improve the consistency of pathology observations between medical images and their corresponding reports.
To enhance the generalizability and robustness of medical visual representation learning without requiring external annotations.

Main Methods:

Proposed a pathological-level cross-modal alignment (PCMA) approach to maximize consistency between visual and textual pathology observations.
Introduced a Visual Pathology Observation Extractor to derive representations from localized image tokens.
Developed a proxy task for correlation exploration among image patches to enrich fine-grained details.

Main Results:

The PLACE framework achieved state-of-the-art performance across multiple downstream tasks.
Demonstrated significant improvements in classification, image-to-text retrieval, semantic segmentation, object detection, and report generation.
The PCMA module showed effectiveness and robustness, independent of external disease annotations.

Conclusions:

The proposed PLACE framework effectively enhances medical visual representation learning by focusing on pathological-level consistency.
Correlation exploration enriches fine-grained details, leading to superior performance on diverse medical applications.
This approach offers a robust and generalizable method for joint learning from medical image-report pairs.