# Classification of cancer pathology reports: a large-scale comparative study

arXiv: https://arxiv.org/abs/2006.16370 · HTML: https://mlsanity.com/paper/2006.16370/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks](https://mlsanity.com/paper/2101.02486/) (2021)
- [From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?](https://mlsanity.com/paper/2403.11894/) (2024)
- [Machine learning classification of non-Markovian noise disturbing quantum dynamics](https://mlsanity.com/paper/2101.03221/) (2021)

## Abstract (arXiv)

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.