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

Code-free machine learning for classification of central nervous system histopathology images.

Journal of neuropathology and experimental neurology - 21 Feb 2023

Jungo Patric, Hewer Ekkehard

Abstract excerpt

Machine learning (ML), an application of artificial intelligence, is currently transforming the analysis of biomedical data and specifically of biomedical images including histopathology. The promises of this technology contrast, however, with its currently limited application in routine clinical practice. This discrepancy is in part due to the extent of informatics expertise typically required for implementation...

Read the complete abstract on PubMed

Topics

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

Share to Artificial Intelligence
Share to Brain Neoplasms
Share to Central Nervous System

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Isocitrate Dehydrogenase

Share to Machine Learning

Share to Mutation

Source

Open in PubMed Open full text

Identifiers

PMID: 36734664
DOI: 10.1093/jnen/nlac131
PMCID: PMC9941804

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Code-free machine learning for classification of central nervous system histopathology images.PMID 36734664Read ↗

Built to last? Reproducibility and Reusability of Deep Learning Algorithms in Computational PathologySemantic proximity Read ↗

DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathologySemantic proximity Read ↗

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

Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.Semantic proximity Read ↗

Aligning computational pathology with clinical practice for colorectal cancerSemantic proximity Read ↗

Machine learning can aid in prediction of IDH mutation from H&E-stained histology slides in infiltrating gliomas.Semantic proximity Read ↗

Use case of no code machine learning tools for medical image classification.Semantic proximity Read ↗

Evaluating reproducibility of AI algorithms in digital pathology with DAPPERSemantic proximity Read ↗

Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology.Semantic proximity Read ↗

Performance of Open-Source LLMs in Challenging Radiological Cases – A Benchmark Study on 1,933 Eurorad Case ReportsSemantic proximity Read ↗

SIENNA: Generalizable Lightweight Machine Learning Platform for Brain Tumor DiagnosticsSemantic proximity Read ↗

Performance of a machine learning model in recognition of normal tissue from histological sectionsSemantic proximity Read ↗

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