Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer

@article{Le2020UtilizingAB,
  title={Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer},
  author={H. Le and Rajarsi R. Gupta and Le Hou and Shahira Abousamra and Danielle Fassler and T. Kurç and D. Samaras and R. Batiste and Tianhao Zhao and A. Dyke and Ashish Sharma and E. Bremer and J. Almeida and J. Saltz},
  journal={The American journal of pathology},
  year={2020}
}
  • H. Le, Rajarsi R. Gupta, +11 authors J. Saltz
  • Published 2020
  • Biology, Engineering, Computer Science, Medicine
  • The American journal of pathology
  • Quantitative assessment of Tumor-TIL spatial relationships is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural network (CNN) analysis pipelines to generate combined maps of cancer regions and tumor infiltrating lymphocytes (TILs) in routine diagnostic breast cancer whole slide tissue images (WSIs). The combined maps provide 1) insight about the structural patterns and spatial distribution of… CONTINUE READING
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    References

    SHOWING 1-10 OF 76 REFERENCES
    Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent
    • 192
    • PDF
    Classification of Breast Cancer Histology using Deep Learning
    • 38
    • PDF
    Automatic histopathology image analysis with CNNs
    • 16
    • PDF
    Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning.
    • 41
    • PDF