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

Weakly supervised learning on unannotated H&E-stained slides predicts BRAF mutation in thyroid cancer with high accuracy.

The Journal of pathology - 1 Nov 2021

Anand Deepak, Yashashwi Kumar, Kumar Neeraj, Rane Swapnil, Gann Peter H, Sethi Amit

Abstract excerpt

Deep neural networks (DNNs) that predict mutational status from H&E slides of cancers can enable inexpensive and timely precision oncology. Although expert knowledge is reliable for annotating regions informative of malignancy and other known histologic patterns (strong supervision), it is unreliable for identifying regions informative of mutational status. This poses a serious impediment to obtaining higher...

Read the complete abstract on PubMed

Topics

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

Share to Aged
Share to AI in cancer detection

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Mutation

Share to Neural Networks, Computer

Share to Proto-Oncogene Proteins B-raf

Share to Radiomics and Machine Learning in Medical Imaging

Share to Staining and Labeling

Share to Thyroid Neoplasms

Source

Identifiers

PMID: 34346511
DOI: 10.1002/path.5773

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

Weakly supervised learning on unannotated H&E-stained slides predicts BRAF mutation in thyroid cancer with high accuracy.PMID 34346511Read ↗

Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology.Semantic proximity Read ↗

Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study.Semantic proximity Read ↗

Annotation-Free Deep Learning-Based Prediction of Thyroid Molecular Cancer Biomarker BRAF (V600E) from Cytological Slides.Semantic proximity Read ↗

Predicting TP53 Biomarkers from Whole Slide Images across Human Solid Tumors Using Weakly Supervised Learning.Semantic proximity Read ↗

XpressO-Melanoma: An Explainable Deep Learning Model for the Prediction of BRAF V600 Mutation Status in Cutaneous MelanomasSemantic proximity Read ↗

Deceptive learning in histopathology.Semantic proximity Read ↗

Classifying driver mutations of papillary thyroid carcinoma on whole slide image: an automated workflow applying deep convolutional neural network.Semantic proximity Read ↗

Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features.Semantic proximity Read ↗

Deep Learning Prediction of TERT Promoter Mutation Status in Thyroid Cancer Using Histologic Images.Semantic proximity Read ↗

[Assessing BRAF status of localized and locally advanced papillary thyroid microcarcinomas using machine learning methods].Semantic proximity Read ↗

Weakly supervised contrastive learning predicts tumor infiltrating macrophages and immunotherapy benefit in breast cancer from unannotated pathology imagesSemantic proximity Read ↗

Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysisSemantic proximity Read ↗

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