  We propose a method to accurately obtain the ratio of tumor cells over an
entire histological slide. We use deep fully convolutional neural network
models trained to detect and classify cells on images of H&E-stained tissue
sections. Pathologists' labels consisting of exhaustive nuclei locations and
tumor regions were used to trained the model in a supervised fashion. We show
that combining two models, each working at a different magnification allows the
system to capture both cell-level details and surrounding context to enable
successful detection and classification of cells as either tumor-cell or
normal-cell. Indeed, by conditioning the classification of a single cell on a
multi-scale context information, our models mimic the process used by
pathologists who assess cell neoplasticity and tumor extent at different
microscope magnifications. The ratio of tumor cells can then be readily
obtained by counting the number of cells in each class. To analyze an entire
slide, we split it into multiple tiles that can be processed in parallel. The
overall tumor cell ratio can then be aggregated. We perform experiments on a
dataset of 100 slides with lung tumor specimens from both resection and tissue
micro-array (TMA). We train fully-convolutional models using heavy data
augmentation and batch normalization. On an unseen test set, we obtain an
average mean absolute error on predicting the tumor cell ratio of less than 6%,
which is significantly better than the human average of 20% and is key in
properly selecting tissue samples for recent genetic panel tests geared at
prescribing targeted cancer drugs. We perform ablation studies to show the
importance of training two models at different magnifications and to justify
the choice of some parameters, such as the size of the receptive field.
