  Like most global cancer registries, the National Cancer Registry in South
Africa employs expert human coders to label pathology reports using appropriate
International Classification of Disease for Oncology (ICD-O) codes spanning 42
different cancer types. The annotation is extensive for the large volume of
cancer pathology reports the registry receives annually from public and private
sector institutions. This manual process, coupled with other challenges results
in a significant 4-year lag in reporting of annual cancer statistics in South
Africa. We present a hierarchical deep learning ensemble method incorporating
state of the art convolutional neural network models for the automatic
labelling of 2201 de-identified, free text pathology reports, with appropriate
ICD-O breast cancer topography codes across 8 classes. Our results show an
improvement in primary site classification over the state of the art CNN model
by greater than 14% for F1 micro and 55% for F1 macro scores. We demonstrate
that the hierarchical deep learning ensemble improves on state-of-the-art
models for ICD-O topography classification in comparison to a flat multiclass
model for predicting ICD-O topography codes for pathology reports.
