  We report about the application of state-of-the-art deep learning techniques
to the automatic and interpretable assignment of ICD-O3 topography and
morphology codes to free-text cancer reports. We present results on a large
dataset (more than 80 000 labeled and 1 500 000 unlabeled anonymized reports
written in Italian and collected from hospitals in Tuscany over more than a
decade) and with a large number of classes (134 morphological classes and 61
topographical classes). We compare alternative architectures in terms of
prediction accuracy and interpretability and show that our best model achieves
a multiclass accuracy of 90.3% on topography site assignment and 84.8% on
morphology type assignment. We found that in this context hierarchical models
are not better than flat models and that an element-wise maximum aggregator is
slightly better than attentive models on site classification. Moreover, the
maximum aggregator offers a way to interpret the classification process.
