Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 24, Issue 11, Pages 3085-3094Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2020.3005016
Keywords
Cancer; Tumors; Surfaces; Morphology; Pathology; Deep learning; Task analysis; Artificial intelligence; attention models; deep learning; hierarchical models; interpretable models; machine learning; natural language processing; oncology; recurrent neural networks
Categories
Funding
- Italian Ministry of Education, University, and Research [2017TWNMH2]
Ask authors/readers for more resources
We report about the application of state-ofthe-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 maximumaggregator offers a way to interpret the classification process.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available