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

Targeting Warburg effect in Chinese hamster ovary cell culture with pyruvate dehydrogenase kinase inhibitors.

Biotechnology progress·2026
Same author

Osmolyte-Based Formulations for Enhanced Thermal Stability of mRNA Drug Substance: A Systematic Screening and Optimization Study.

Pharmaceutical research·2026
Same author

Structural Elucidation of Fc- and Fab-Associated <i>N</i>-Glycans in Cetuximab Using Protein A-Assisted Domain-Resolved Glycan Profiling Using Mass Spectrometry.

Journal of the American Society for Mass Spectrometry·2026
Same author

Artificial Intelligence for Molecular Subtyping in Unresectable Gallbladder Cancer: A Proof-of-Concept Study for CT-based HER2 Status Prediction.

Journal of clinical and experimental hepatology·2026
Same author

Bioinformatic pipeline to identify potential therapeutic targets with subsequent isolation and characterization of novel human anti- DDR1 antibodies.

Scientific reports·2026
Same author

Rapid Identification of Counterfeit Biopharmaceuticals using Portable Fourier Transform Infrared Spectroscopy.

AAPS PharmSciTech·2026

Related Experiment Video

Updated: Jun 13, 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

Weakly supervised large-scale pancreatic cancer detection using multi-instance learning.

Shyamapada Mandal1, Keerthiveena Balraj2, Hariprasad Kodamana1,2

  • 1Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India.

Frontiers in Oncology
|September 13, 2024
PubMed
Summary
This summary is machine-generated.

A new two-stage deep learning model accurately detects pancreatic tumors on CT scans. This advancement offers improved early detection for pancreatic cancer, a disease lacking effective screening methods.

Keywords:
feature extractionimage segmentationmedical image analysismulti-instance learningpancreatic cancer

More Related Videos

Utilizing High Resolution Ultrasound to Monitor Tumor Onset and Growth in Genetically Engineered Pancreatic Cancer Models
06:57

Utilizing High Resolution Ultrasound to Monitor Tumor Onset and Growth in Genetically Engineered Pancreatic Cancer Models

Published on: April 7, 2018

10.8K
Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture
16:59

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

Published on: March 4, 2011

12.3K

Related Experiment Videos

Last Updated: Jun 13, 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
Utilizing High Resolution Ultrasound to Monitor Tumor Onset and Growth in Genetically Engineered Pancreatic Cancer Models
06:57

Utilizing High Resolution Ultrasound to Monitor Tumor Onset and Growth in Genetically Engineered Pancreatic Cancer Models

Published on: April 7, 2018

10.8K
Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture
16:59

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

Published on: March 4, 2011

12.3K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Early detection of pancreatic cancer remains a significant clinical challenge.
  • Current diagnostic methods for pancreatic cancer are limited, often leading to late-stage diagnosis and reduced treatment efficacy.

Purpose of the Study:

  • To develop and evaluate a novel two-stage deep learning model for the early detection of pancreatic tumors using computed tomography (CT) images.
  • To improve the accuracy and efficiency of identifying pancreatic cancer compared to existing methods.

Main Methods:

  • A two-stage weakly supervised deep learning model was developed.
  • The first stage utilized nnU-Net for pancreas segmentation on Memorial Sloan Kettering Cancer Center (MSKCC) data.
  • The second stage employed a multi-instance learning classifier on Henry Ford Health (HFH) data for tumor detection.

Main Results:

  • The proposed model achieved high performance metrics: accuracy of 0.907, sensitivity of 0.905, specificity of 0.908, and AUC (ROC) of 0.903.
  • The two-stage framework demonstrated an improved ability to differentiate pancreatic tumors from non-tumor pancreas in CT images.
  • The model showed significantly enhanced performance compared to other reported studies.

Conclusions:

  • The developed two-stage deep learning architecture shows significant promise for enhanced pancreatic tumor detection using CT imaging.
  • This approach offers a potential breakthrough in addressing the challenge of early pancreatic cancer diagnosis.
  • Further validation and integration into clinical workflows could improve patient outcomes.