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Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Variation in the Incidence and Magnitude of Tumor-Infiltrating Lymphocytes in Breast Cancer Subtypes
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Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations
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Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association
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Can You Trust Predictive Uncertainty Under Real Dataset Shifts in Digital Pathology?
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Quantitative digital image analysis of tumor-infiltrating lymphocytes in HER2-positive breast cancer
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Reproducibility and predictive value of scoring stromal tumour infiltrating lymphocytes in triple-negative breast cancer: a multi-institutional study
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Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer
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Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics
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Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
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A Computational Tumor-Infiltrating Lymphocyte Assessment Method Comparable with Visual Reporting Guidelines for Triple-Negative Breast Cancer
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What do we still need to learn on digitally assessed biomarkers?
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Locally advanced breast cancer: Tumor-infiltrating lymphocytes as a predictive factor of response to neoadjuvant chemotherapy
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A survey on deep learning in medical image analysis
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Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
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Learning to detect lymphocytes in immunohistochemistry with deep learning
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Deep neural network models for computational histopathology: A survey
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Residual cyclegan for robust domain transformation of histopathological tissue slides
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Breast cancer genomics and immuno-oncological markers to guide immune therapies
inferredcites →https://doi.org/10.1038/nrclinonc.2015.215openalex-snapshot
Clinical relevance of host immunity in breast cancer: from TILs to the clinic
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The impact of site-specific digital histology signatures on deep learning model accuracy and bias
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Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group
inferredcites →https://doi.org/10.1038/s41523-020-0155-1openalex-snapshot
Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials
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Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer
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Designing deep learning studies in cancer diagnostics
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Deep learning in histopathology: the path to the clinic
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QuPath: Open source software for digital pathology image analysis
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Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer
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The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014
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Structured crowdsourcing enables convolutional segmentation of histology images
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1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
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Computerized Image-Based Detection and Grading of Lymphocytic Infiltration in HER2+ Breast Cancer Histopathology
inferredcites →https://doi.org/10.1111/1759-7714.13639openalex-snapshot
Quantitative multiplex immunofluorescence analysis identifies infiltrating PD1+CD8+ and CD8+ T cells as predictive of response to neoadjuvant chemotherapy in breast cancer
inferredcites →https://doi.org/10.1117/12.2512892openalex-snapshot
Joint region and nucleus segmentation for characterization of tumor infiltrating lymphocytes in breast cancer
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Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling
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An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
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Abstract PD9-05: Stromal tumor-infiltrating lymphocytes identify early-stage triple-negative breast cancer patients with favorable outcomes at 10-year follow-up in the absence of systemic therapy: a pooled analysis of 1835 patients
inferredcites →https://doi.org/10.1159/000453569openalex-snapshot
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
inferredcites →https://doi.org/10.1186/s40425-016-0165-6openalex-snapshot
Clinical significance of tumor-infiltrating lymphocytes in breast cancer
inferredcites →https://doi.org/10.1200/cci.19.00126openalex-snapshot
Deep-Learning–Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data
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Tumor-Infiltrating Lymphocytes and Prognosis: A Pooled Individual Patient Analysis of Early-Stage Triple-Negative Breast Cancers
inferredcites →https://doi.org/10.1200/jco.2006.05.9584openalex-snapshot
Quantification of Regulatory T Cells Enables the Identification of High-Risk Breast Cancer Patients and Those at Risk of Late Relapse
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Multi-panel immunofluorescence analysis of tumor infiltrating lymphocytes in triple negative breast cancer: Evolution of tumor immune profiles and patient prognosis
inferredcites →https://doi.org/10.2147/ott.s126424openalex-snapshot
Mechanism of immune evasion in breast cancer
inferredcites →https://doi.org/10.3332/ecancer.2019.924openalex-snapshot
Highlights of the 16th St Gallen International Breast Cancer Conference, Vienna, Austria, 20–23 March 2019: personalised treatments for patients with early breast cancer
inferredcites →https://doi.org/10.3389/fonc.2021.806603openalex-snapshot
Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer
inferredcites →https://doi.org/10.3390/cancers13123050openalex-snapshot
Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers
inferredcites →https://doi.org/10.3390/cancers13174410openalex-snapshot
Intra-Tumour Heterogeneity Is One of the Main Sources of Inter-Observer Variation in Scoring Stromal Tumour Infiltrating Lymphocytes in Triple Negative Breast Cancer
inferredcites →https://doi.org/10.3390/cancers14092148openalex-snapshot
Spatial Characterization of Tumor-Infiltrating Lymphocytes and Breast Cancer Progression
inferredcites →https://doi.org/10.4103/2153-3539.186902openalex-snapshot
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
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Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection
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A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study
inferredcites →https://doi.org/10.5858/arpa.2018-0343-raopenalex-snapshot
A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer
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Recent Advances in Pathology: the 2023 Annual Review Issue of The Journal of Pathology
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