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 journal

Multimodule Human-Artificial Intelligence Collaboration Pipeline for Large Language Model-Assisted Thematic Analysis Across Digital Health Interview Studies: Comparative Evaluation Study.

JMIR medical informatics·2026
Same journal

Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study.

JMIR medical informatics·2026
Same journal

Intrasystem Repeatability of S-Detect for Breast Ultrasound Classification With Identical Static Images: Single-Center Retrospective Repeatability Study.

JMIR medical informatics·2026
Same journal

Clinician Perspectives on Ambient AI Scribes in the Intensive Care Unit: Qualitative Interview Study.

JMIR medical informatics·2026
Same journal

IdeaDistiller-AI Support for Idea Synthesis in Concept Mapping: Algorithm Development and Validation Study.

JMIR medical informatics·2026
Same journal

Pregnancy-Related Clinical Codes in Unlikely Populations in Primary Care.

JMIR medical informatics·2026
See all related articles

Related Experiment Video

Updated: Jan 13, 2026

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

13.0K

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model:

Jin Zhao1, Xiaolian Wen1, Li Ma1

  • 1Department of Hematology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, No. 3, Zhigong New Street, Taiyuan, 030013, China, 86 0351-4650984.

JMIR Medical Informatics
|January 6, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning U-Net model for accurate lymphoma subtype classification. The model enhances diagnostic precision and efficiency, aiding clinical decision-making in pathology.

Keywords:
AIU-Netartificial intelligenceautomated diagnosisdeep learninglymphomamedical image analysispathological subtype

More Related Videos

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

3.2K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

482

Related Experiment Videos

Last Updated: Jan 13, 2026

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

13.0K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

3.2K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

482

Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Artificial Intelligence in Medicine

Background:

  • Accurate lymphoma classification is crucial for treatment planning.
  • Traditional methods are subjective and inefficient, necessitating automated solutions.
  • Deep learning offers a promising approach for objective and efficient diagnostics.

Purpose of the Study:

  • To investigate the U-Net deep learning model for classifying and grading lymphoma subtypes.
  • To enhance diagnostic precision and efficiency in lymphoma pathology.
  • To develop an automated solution for challenging diagnostic tasks.

Main Methods:

  • Utilized the U-Net model with attention mechanisms and residual networks for segmentation and classification.
  • Processed 620 histopathological images from The Cancer Genome Atlas and Cancer Imaging Archive.
  • Employed data augmentation and five-fold cross-validation for model robustness and generalization.

Main Results:

  • The U-Net model achieved high segmentation accuracy, improving input quality for classification.
  • Achieved 92% accuracy, 91.04% sensitivity, and 89.04% specificity in classifying 3 lymphoma subtypes.
  • Demonstrated strong clinical applicability as an assistive diagnostic tool with an AUC of 0.95.

Conclusions:

  • Deep learning with U-Net architecture significantly improves lymphoma subtype classification and grading.
  • The model provides efficient and precise support for clinical decision-making.
  • Future work includes multicenter validation and integration into digital pathology workflows.