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

https://doi.org/10.1002/path.5773
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cncr.23843openalex-snapshot
Guideline implementation for breast healthcare in low- and middle-Income countries: Treatment resource allocation
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Morphological parameters able to predict BRAFV600E ‐mutated malignancies on thyroid fine‐needle aspiration cytology: Our institutional experience
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Morphology predicts BRAF V600E mutation in papillary thyroid carcinoma: an interobserver reproducibility study
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A Systematic Review and Meta-Analysis of the Diagnostic Performance of BRAF V600E Immunohistochemistry in Thyroid Histopathology
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Weakly supervised histopathology cancer image segmentation and classification
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Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
inferredcites →https://doi.org/10.1016/j.media.2020.101814openalex-snapshot
Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images
inferredcites →https://doi.org/10.1016/s1726-4901(10)70025-3openalex-snapshot
BRAF Mutation in Papillary Thyroid Carcinoma: Pathogenic Role and Clinical Implications
inferredcites →https://doi.org/10.1038/s41379-020-00724-3openalex-snapshot
Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features
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Independent Clonal Origins of Distinct Tumor Foci in Multifocal Papillary Thyroid Carcinoma
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Discovery of a selective inhibitor of oncogenic B-Raf kinase with potent antimelanoma activity
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Combination Treatment with the BRAF V600E Inhibitor Vemurafenib and the BH3 Mimetic Navitoclax for BRAF -Mutant Thyroid Carcinoma
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A benchmark for comparing precision medicine methods in thyroid cancer diagnosis using tissue microarrays
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Determinants of BRAF Mutations in Primary Melanomas
inferredcites →https://doi.org/10.1097/pai.0000000000000502openalex-snapshot
Virtual Double Staining: A Digital Approach to Immunohistochemical Quantification of Estrogen Receptor Protein in Breast Carcinoma Specimens
inferredcites →https://doi.org/10.1097/sla.0b013e318148563dopenalex-snapshot
The Prevalence and Prognostic Value of BRAF Mutation in Thyroid Cancer
inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvpr.2015.7298685openalex-snapshot
The application of two-level attention models in deep convolutional neural network for fine-grained image classification
inferredcites →https://doi.org/10.1109/cvpr.2016.266openalex-snapshot
Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification
inferredcites →https://doi.org/10.1109/cvpr31182.2015openalex-snapshot
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Fast GPU-Enabled Color Normalization for Digital Pathology
inferredcites →https://doi.org/10.1109/iwssip46987.2019openalex-snapshot
2019 International Conference on Systems, Signals and Image Processing (IWSSIP)
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Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images
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Demonstration of a Genetic Therapeutic Index for Tumors Expressing Oncogenic BRAF by the Kinase Inhibitor SB-590885
inferredcites →https://doi.org/10.1158/1078-0432.ccr-1118-3openalex-snapshot
BRAF Mutation Is Frequently Present in Sporadic Colorectal Cancer with Methylated hMLH1, But Not in Hereditary Nonpolyposis Colorectal Cancer
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-snapshot
Long Short-Term Memory
inferredcites →https://doi.org/10.1186/1471-2105-12-77openalex-snapshot
pROC: an open-source package for R and S+ to analyze and compare ROC curves
inferredcites →https://doi.org/10.1186/1476-4598-5-2openalex-snapshot
BRAF mutations are associated with distinctive clinical, pathological and molecular features of colorectal cancer independently of microsatellite instability status
inferredcites →https://doi.org/10.1200/jco.2014.56.8253openalex-snapshot
Association Between BRAF V600E Mutation and Recurrence of Papillary Thyroid Cancer
inferredcites →https://doi.org/10.1200/jco.2018.36.15_suppl.e21561openalex-snapshot
Using deep learning algorithms on histopathology images for the prediction of BRAF and NRAS mutations in invasive melanoma.
inferredcites →https://doi.org/10.1210/js.2017-00097openalex-snapshot
Rising Thyroid Cancer Incidence in Southern India: An Epidemic of Overdiagnosis?
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Immunohistochemistry cannot replace DNA analysis for evaluation ofBRAFV600E mutations in papillary thyroid carcinoma
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Effective Approaches to Attention-based Neural Machine Translation
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inferredcites →https://doi.org/10.2353/jmoldx.2006.060070openalex-snapshot
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inferredcites →https://doi.org/10.3390/jcm8101675openalex-snapshot
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inferredcites →https://doi.org/10.4103/2153-3539.179984openalex-snapshot
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Difference between papillary and follicular thyroid carcinoma outcomes: an experience from Egyptian institution.

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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inferred← citeshttps://doi.org/10.1002/path.6163openalex-snapshot
Computational pathology in cancer diagnosis, prognosis, and prediction – present day and prospects
inferred← citeshttps://doi.org/10.1002/path.6168openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s12022-023-09796-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-443-13809-6.00008-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.bspc.2025.108402openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.cll.2026.06.014openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compbiomed.2023.107083openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compmedimag.2023.102189openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.critrevonc.2026.105588openalex-snapshot
Spatial heterogeneity in anaplastic thyroid carcinoma: Mechanistic insights and clinical translation through single-cell and spatial transcriptomics
inferred← citeshttps://doi.org/10.1016/j.eprac.2025.07.020openalex-snapshot
Deep Learning for the Diagnosis and Treatment of Thyroid Cancer: A Review
inferred← citeshttps://doi.org/10.1016/j.gie.2022.04.057openalex-snapshot
Artificial intelligence for disease diagnosis: the criterion standard challenge
inferred← citeshttps://doi.org/10.1016/j.jncc.2022.11.004openalex-snapshot
Intelligent oncology: The convergence of artificial intelligence and oncology
inferred← citeshttps://doi.org/10.1016/j.jpi.2023.100306openalex-snapshot
Efficient quality control of whole slide pathology images with human-in-the-loop training
inferred← citeshttps://doi.org/10.1016/j.jpi.2023.100357openalex-snapshot
Computational pathology: A survey review and the way forward
inferred← citeshttps://doi.org/10.1016/j.modpat.2023.100296openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.talanta.2023.124753openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-024-78459-3openalex-snapshot
A digital pathology model for predicting radioiodine-avid metastases on initial post-therapeutic 131I scan in patients with papillary thyroid cancer
inferred← citeshttps://doi.org/10.1038/s41746-025-01524-2openalex-snapshot
AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology
inferred← citeshttps://doi.org/10.1038/s43856-024-00471-5openalex-snapshot
A systematic pan-cancer study on deep learning-based prediction of multi-omic biomarkers from routine pathology images
inferred← citeshttps://doi.org/10.1088/1361-6560/ac910aopenalex-snapshot
Towards label-efficient automatic diagnosis and analysis: a comprehensive survey of advanced deep learning-based weakly-supervised, semi-supervised and self-supervised techniques in histopathological image analysis
inferred← citeshttps://doi.org/10.1089/ct.2025;37.331-335openalex-snapshot
Predicting Genetic Mutations in PTC with Artificial Intelligence–Guided Analysis of Histopathology
inferred← citeshttps://doi.org/10.1089/thy.2023.0132openalex-snapshot
Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions
inferred← citeshttps://doi.org/10.1089/thy.2024.0691openalex-snapshot
Deep Learning Discovers New Morphological Features while Predicting Genetic Alterations from Histopathology of Papillary Thyroid Carcinoma
inferred← citeshttps://doi.org/10.1093/bib/bbad151openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/noajnl/vdad119openalex-snapshot
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inferred← citeshttps://doi.org/10.1101/2022.01.21.477189openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/iccvw60793.2023.00427openalex-snapshot
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inferred← citeshttps://doi.org/10.1136/jcp-2026-210720openalex-snapshot
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inferred← citeshttps://doi.org/10.1158/2767-9764.crc-24-0397openalex-snapshot
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inferred← citeshttps://doi.org/10.1177/00034894231158464openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s12885-026-16144-6openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s13000-025-01618-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1210/clinem/dgae277openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fonc.2023.958310openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fonc.2024.1275769openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/ani13152404openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/ijms24032521openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/jpm12122022openalex-snapshot
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inferred← citeshttps://doi.org/10.34133/2022/9786242openalex-snapshot
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inferred← citeshttps://doi.org/10.54254/2753-8818/2024.19820openalex-snapshot
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inferred← citeshttps://doi.org/10.5455/aim.2023.31.280-286openalex-snapshot
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