Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning.

Sensors (Basel, Switzerland) - 17 May 2021

Im Sanghyuk, Hyeon Jonghwan, Rha Eunyoung, Lee Janghyeon, Choi Ho-Jin, Jung Yuchae, Kim Tae-Jung

Abstract excerpt

Diffuse gliomas are the most common primary brain tumors and they vary considerably in their morphology, location, genetic alterations, and response to therapy. In 2016, the World Health Organization (WHO) provided new guidelines for making an integrated diagnosis that incorporates both morphologic and molecular features to diffuse gliomas. In this study, we demonstrate how deep learning approaches can be used...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Brain Neoplasms
Share to Glioma

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Machine Learning

Share to Mutation

Share to World Health Organization

Source

Open in PubMed Open full text

Identifiers

PMID: 34067934
DOI: 10.3390/s21103500
PMCID: PMC8156672

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning.PMID 34067934Read ↗

Predicting Prognosis and IDH Mutation Status for Patients with Lower-Grade Gliomas Using Whole Slide ImagesSemantic proximity Read ↗

Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images.Semantic proximity Read ↗

The Evolving Classification of Diffuse Gliomas: World Health Organization Updates for 2021.Semantic proximity Read ↗

Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma.Semantic proximity Read ↗

A novel approach combined transfer learning and deep learning to predict TMB from histology imageSemantic proximity Read ↗

Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging.Semantic proximity Read ↗

WES data from 286 diffuse gliomas under the 2021 WHO Classification of Tumors of the Central Nervous System.Semantic proximity Read ↗

Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patientsSemantic proximity Read ↗

Insight into deep learning for glioma IDH medical image analysis: A systematic review.Semantic proximity Read ↗

Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.Semantic proximity Read ↗

Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for A Comprehensive Learning, Discovering, and Quantifying Microenvironmental FeaturesSemantic proximity Read ↗

102 AI-Based Molecular Classification of Diffuse Gliomas using Rapid, Label-Free Optical Imaging.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.