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Classification of cancer pathology reports: a large-scale comparative study
  • Stefano Martina ,
  • Leonardo Ventura ,
  • Paolo Frasconi
Stefano Martina
University of Florence

Corresponding Author:[email protected]

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Leonardo Ventura
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Paolo Frasconi
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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) for which we obtained the approval from the institutional ethics committee (CEAV 14081 oss 27/11/2018). 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.
Nov 2020Published in IEEE Journal of Biomedical and Health Informatics volume 24 issue 11 on pages 3085-3094. 10.1109/JBHI.2020.3005016