Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Determining the age cut-off for increased surgical risk after Roux-en-Y gastric bypass in elderly patients: MBSAQIP 2015-2023 database analysis.

Surgical endoscopy·2026

Same author

Effectiveness and safety of extended upadacitinib induction therapy in adult patients with moderate-to-severe active ulcerative colitis.

Crohn's & colitis 360·2026

Same author

Best-first search-based approach for mining top-k closed frequent itemsets from uncertain databases.

PloS one·2026

Same author

Microcystic lymphatic malformation of the small intestine presenting as acute abdominal pain in a 5-year-old boy: a case report and review of the literature.

Journal of medical case reports·2026

Same author

Reliability of Behavioral and fNIRS Neural Responses: Assessments During Posture-Inhibitory Control Dual Tasking.

NeuroSci·2026

Same author

Practical synthesis and photophysical characterization of full-color circularly polarized luminescent 4,7-diaryl-5,6-(<i>R</i>/<i>S</i>)-BINOL-<i>O</i>-[2,1,3]benzothiadiazoles.

RSC advances·2026

Same journal

A simple method for locating and calibrating the keyhole for the retrosigmoid approach using CT plain scans.

Current medical imaging·2026

Same journal

Multimodal Imaging of a Giant Ovarian Mature Cystic Teratoma Featuring the Floating Ball Sign: A Case Report.

Current medical imaging·2026

Same journal

Accurate Segmentation and Three-dimensional Reconstruction Algorithm of Spinal Cord Injury Lesions Based on Multimodal Magnetic Resonance Imaging.

Current medical imaging·2026

Same journal

A Comprehensive Review of Radiomics in Pulmonary Nodule Management: Clinical Applications and Standardization Dilemmas.

Current medical imaging·2026

Same journal

The Value of a Predictive Model Based on Multimodal Ultrasound Imaging Biomarkers Combined with Clinical Features in the Diagnosis of Thyroid Nodules.

Current medical imaging·2026

Same journal

The Prognostic and Mutational Characteristics of Multiple Early-stage Lung Cancers Manifesting as Subsolid Nodules.

Current medical imaging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 25, 2025

Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization

Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization

Published on: December 2, 2013

Robust Engineering-based Unified Biomedical Imaging Framework for Liver Tumor Segmentation.

Vuong Pham¹, Hai Nguyen^2,3, Bao Pham¹

¹Faculty of Information Technology, Sai Gon University, Ho Chi Minh City, Vietnam.

Current Medical Imaging

|August 5, 2021

Summary

This summary is machine-generated.

This study introduces an AI framework for liver tumor identification using computed tomography and whole-slide images. The deep learning approach enhances early cancer diagnosis, potentially reducing pathologist workload.

Keywords:

Tumor segmentation deep learning framework histopathology neural networks radiology

More Related Videos

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

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

Published on: October 10, 2018

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

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

Published on: June 3, 2022

Related Experiment Videos

Last Updated: Oct 25, 2025

Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization

Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization

Published on: December 2, 2013

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

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

Published on: October 10, 2018

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

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

Published on: June 3, 2022

Area of Science:

Artificial Intelligence
Medical Imaging
Computer Vision

Background:

Computer vision and semantic segmentation have advanced significantly.
Medical imaging presents new avenues for AI research.
Early cancer diagnosis is critical for patient outcomes, as cancer is a leading cause of death.

Purpose of the Study:

To develop an AI-driven approach for liver tumor identification.
To leverage advancements in deep learning for medical image analysis.
To improve the speed and accuracy of cancer diagnosis.

Main Methods:

Utilized deep neural network-based methods for image analysis.
Improved upon existing U-Net and GLNet architectures.
Integrated sub-modules with segmentation models to enhance performance.

Main Results:

Presented a novel approach for liver tumor identification.
Applied the method to both computed tomography and whole-slide images.
Demonstrated boosted performance during inference phases.

Conclusions:

The proposed unified framework shows promise for production environments.
AI in medical imaging can aid in early cancer detection.
Deep learning methods can potentially reduce diagnosis time for pathologists.