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

Evaluation of [18F]MFPBG: a novel automatically radiolabeled probe for NET PET imaging.

EJNMMI research·2026
Same author

Bioresponsive microneedle stent provides anastomosis and postoperative adjuvant therapy in preclinical resectable intestinal diseases.

Science translational medicine·2026
Same author

Astragalus polysaccharides alleviate oxidative damage by activating the Keap1-Nrf2 antioxidant pathway through miR-183-5p in a fish cell model.

Fish & shellfish immunology·2026
Same author

Metabolomic signatures of dietary carbohydrates and differential association with type 2 diabetes.

Nature health·2026
Same author

A stratified urine-based molecular diagnostic and prognostic model for non-muscle-invasive bladder cancer management.

BMC cancer·2026
Same author

Mechanistic insights into aroma aging of ripened Pu-erh tea during long-term storage.

Food chemistry·2026

Related Experiment Video

Updated: Jun 13, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.4K

Learnable prototype-guided multiple instance learning for detecting tertiary lymphoid structures in multi-cancer

Pengfei Xia1, Dehua Chen1, Huimin An2

  • 1College of Computer Science and Technology, Donghua University, Shanghai 201620, China.

Medical Image Analysis
|May 30, 2025
PubMed
Summary

Tertiary lymphoid structures (TLS) detection in cancer images is improved by a new framework, LPGMIL. This method effectively identifies sparse and diverse TLS, enhancing prognostic predictions and immunotherapy response assessment.

Keywords:
Learnable prototypeMultiple instance learningTertiary lymphoid structuresWhole-slide pathological images

More Related Videos

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

1.4K
Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

3.6K

Related Experiment Videos

Last Updated: Jun 13, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.4K
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

1.4K
Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

3.6K

Area of Science:

  • Pathology
  • Computational Biology
  • Medical Imaging

Background:

  • Tertiary lymphoid structures (TLS) are critical in tumor microenvironments (TME), influencing patient prognosis and immunotherapy response.
  • Accurate TLS detection in whole-slide pathological images (WSIs) is vital for clinical decisions.
  • Existing multiple instance learning (MIL) methods have limitations in detecting sparse and heterogeneous TLS.

Purpose of the Study:

  • To develop a weakly supervised framework for robust TLS detection in WSIs.
  • To address the challenges of TLS sparsity and heterogeneity in diverse cancer types.
  • To improve the generalizability of MIL for TLS analysis across different malignancies.

Main Methods:

  • Proposed Learnable Prototype-Guided Multiple Instance Learning (LPGMIL) framework.
  • Utilized lymphocyte-dense instances to create learnable global prototypes for feature refinement.
  • Employed multiple learnable global prototypes to capture diverse TLS patterns within WSIs.
  • Validated the framework on a comprehensive six-cancer-type TCGA dataset.

Main Results:

  • LPGMIL demonstrated superior performance compared to existing methods on a multi-cancer dataset.
  • Achieved high accuracy (76.6%), recall (74.1%), F1-score (82.7%), and AUC (83.5%).
  • Effectively handled the sparsity and heterogeneity of TLS in WSIs.

Conclusions:

  • LPGMIL offers an effective solution for weakly supervised TLS detection in complex cancer datasets.
  • The framework enhances the analysis of TLS, crucial for predicting patient outcomes and treatment efficacy.
  • This approach advances computational pathology for precision oncology.