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

arXiv: https://arxiv.org/abs/1905.10841 · HTML: https://mlsanity.com/paper/1905.10841/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

## Built on by (most cited first, 12 of 12)

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- [Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodes](https://mlsanity.com/paper/2301.05938/) (2023)
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- [Optimizing Lymphocyte Detection in Breast Cancer Whole Slide Imaging through Data-Centric Strategies](https://mlsanity.com/paper/2405.13710/) (2024)
- [Contrastive Deep Learning Reveals Age Biomarkers in Histopathological Skin Biopsies](https://mlsanity.com/paper/2411.16956/) (2024)

## Abstract (arXiv)

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). We produce interactive whole slide maps that provide 1) insight about the structural patterns and spatial distribution of lymphocytic infiltrates and 2) facilitate improved quantification of TILs. We evaluated both tumor and TIL analyses using three CNN networks - Resnet-34, VGG16 and Inception v4, and demonstrated that the results compared favorably to those obtained by what believe are the best published methods. We have produced open-source tools and generated a public dataset consisting of tumor/TIL maps for 1,015 TCGA breast cancer images. We also present a customized web-based interface that enables easy visualization and interactive exploration of high-resolution combined Tumor-TIL maps for 1,015TCGA invasive breast cancer cases that can be downloaded for further downstream analyses.