  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.
