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

• URL: http://arxiv.org/abs/2006.16370v1
• Date: Mon, 29 Jun 2020 20:47:33 GMT
• Title: Classification of cancer pathology reports: a large-scale comparative study
• Authors: Stefano Martina, Leonardo Ventura, Paolo Frasconi
• Abstract summary: We apply state-of-the-art deep learning techniques to the automatic 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.
• Score: 8.211700929845689
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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