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

[Study of electroreflectance spectrum and Franz-Keldysh effect at metal-GaAs interfaces].

Guang pu xue yu guang pu fen xi = Guang pu·2008
Same author

[Study on electro-degradation of new conjugated polymer PFO-BT15 light emitting diodes].

Guang pu xue yu guang pu fen xi = Guang pu·2008
Same author

Comparison of the curative effects of video assisted thoracoscopic anterior correction and small incision, thoracotomic anterior correction for idiopathic thoracic scoliosis.

Chinese medical journal·2008
Same author

Distribution and sources of mercury in soils from former industrialized urban areas of Beijing, China.

Environmental monitoring and assessment·2008
Same author

[Main flavonoids from Sophora flavescenes].

Yao xue xue bao = Acta pharmaceutica Sinica·2008
Same author

External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean.

Journal of chemical information and modeling·2008

Related Experiment Video

Updated: Jul 8, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Consistent Learning-Based Breast Tumor Segmentation and Its Application in Sentinel Lymph Node Metastasis Prediction.

Fengjun Zhao, Kaiming Huang, Zhipeng Sun

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary

    We developed a novel Multi-scale RepVGG-based Segmentation Network (MPSegNet) for accurate breast tumor segmentation in MR images. Our consistent learning framework improves segmentation and aids in predicting lymph node metastasis.

    More Related Videos

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

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

    Published on: October 10, 2018

    8.3K
    Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
    09:53

    Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

    Published on: August 16, 2020

    7.3K

    Related Experiment Videos

    Last Updated: Jul 8, 2025

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    6.8K
    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

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

    Published on: October 10, 2018

    8.3K
    Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
    09:53

    Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

    Published on: August 16, 2020

    7.3K

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Oncology

    Background:

    • Accurate breast tumor segmentation is vital for staging lymph nodes in breast cancer patients.
    • Current segmentation methods struggle with variations in tumor size, image quality, and noisy annotations.
    • Developing robust segmentation techniques is crucial for improving diagnostic accuracy.

    Purpose of the Study:

    • To develop a Multi-scale RepVGG-based Segmentation Network (MPSegNet) for segmenting breast tumors from MR images.
    • To implement a consistent learning framework to mitigate the impact of noisy labels on segmentation.
    • To analyze the relationship between segmentation performance and the prediction of sentinel lymph node (SLN) metastasis.

    Main Methods:

    • Developed MPSegNet, a novel deep learning model incorporating a multi-scale RepVGG backbone.
    • Constructed a consistent learning framework ensuring identical segmentation predictions from different views of the same tumor.
    • Evaluated segmentation accuracy and its correlation with SLN metastasis prediction performance.

    Main Results:

    • MPSegNet demonstrated superior performance compared to existing state-of-the-art methods.
    • Consistent learning significantly enhanced breast tumor segmentation accuracy.
    • Optimal segmentation did not directly correlate with the best SLN metastasis prediction, indicating a complex relationship.

    Conclusions:

    • The proposed MPSegNet with consistent learning offers improved breast tumor segmentation in MR images.
    • The study highlights the importance of investigating the intricate relationship between tumor segmentation and metastasis prediction for precise patient care.
    • This work has potential significance for enhancing the medical care of breast cancer patients through accurate segmentation and predictive analysis.