Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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

Impact of peritoneal lavage on intra-abdominal abscess after laparoscopic appendectomy for perforated appendicitis: a propensity score matching analysis.

BMC gastroenterology·2025
Same author

Anisotropic Tactile Sensors: Constructive Designs, Challenges, and Emerging Applications.

Chem & bio engineering·2025
Same author

Do environmental values drive artificial intelligence products green purchasing behavior? A value-attitude-behavior approach.

Acta psychologica·2025
Same author

Gradient Porous Flexible Pressure Sensors with the Relay Effect for High-Accuracy Braille-to-Speech Recognition.

ACS sensors·2025
Same author

Long-term efficacy and anamnestic response of hepatitis B vaccine derived from Chinese hamster ovary cell after 18-20 years.

Vaccine·2025
Same author

Electrocatalytic nitrate reduction using iron single atoms for sustainable ammonium supplies to increase rice yield.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: May 15, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.3K

Lung Cancer Screening Classification by Sequential Multi-Instance Learning (SMILE) Framework With Multiple CT Scans.

Wangyuan Zhao, Yuanyuan Fu, Yujia Shen

    IEEE Transactions on Medical Imaging
    |April 9, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new AI framework for lung cancer detection using sequential CT scans. The method accurately predicts malignancy from multiple images without needing nodule location data, aiding early diagnosis.

    More Related Videos

    Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
    10:26

    Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

    Published on: May 19, 2023

    1.7K
    Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
    08:05

    Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

    Published on: December 19, 2020

    14.0K

    Related Experiment Videos

    Last Updated: May 15, 2025

    Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
    07:53

    Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

    Published on: October 13, 2023

    1.3K
    Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
    10:26

    Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

    Published on: May 19, 2023

    1.7K
    Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
    08:05

    Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

    Published on: December 19, 2020

    14.0K

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Oncology

    Background:

    • Lung cancer screening via computed tomography (CT) enhances survival rates through early detection of pulmonary nodules.
    • Sequential CT scans are vital for assessing nodule malignancy and improving lung cancer detection.
    • Accurate classification algorithms for multiple CT images are needed, ideally without requiring radiologists to annotate nodule locations.

    Purpose of the Study:

    • To propose the sequential multi-instance learning (SMILE) framework for predicting high-risk lung cancer patients using multiple CT scans.
    • To develop an effective lung cancer classification algorithm that operates on multiple images without nodule location annotations.
    • To reduce the burden on radiologists by automating the analysis of sequential CT scans for lung cancer prediction.

    Main Methods:

    • The SMILE framework involves two main steps: nodule instance generation using a detection algorithm and image category transformation, followed by nodule malignancy prediction.
    • Multi-instance learning is combined with temporal feature extraction within a fusion framework to enhance classification performance.
    • The approach was evaluated using five-fold cross-validation on a dataset of 925 patients (182 malignant, 743 benign), with each patient having three CT scans taken approximately one year apart.

    Main Results:

    • The proposed SMILE framework demonstrated effective performance in predicting lung cancer risk from sequential CT scans.
    • The method successfully predicted nodule malignancy using patient-level annotations, without requiring precise nodule location data.
    • Experimental results validated the potential of SMILE to streamline the analysis process for radiologists.

    Conclusions:

    • The sequential multi-instance learning (SMILE) framework offers a promising approach for lung cancer classification from multiple CT scans.
    • SMILE effectively utilizes temporal information and multi-instance learning to improve diagnostic accuracy without manual nodule annotation.
    • This framework has the potential to significantly aid radiologists in early lung cancer detection and patient risk stratification.