Classification of Cancer Pathology Reports: A Large-Scale Comparative Study

Abstract

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 maximum aggregator offers a way to interpret the classification process.

Machine learning classification of non-Markovian noise disturbing quantum dynamics

“…where τ r defines the number of elements of the r-th sequence. RNNs can be used in tasks regarding Natural Language Processing (NLP) [48,[77][78][79], time series analysis [80] and, in general, all the tasks involving ordered set of data [81]. Note that, in general, for sequence-to-sequence problems also y can be a sequence of elements, as for example in machine translation where the inputs and outputs of the RNN are sentences in different languages [82].…”

Deep learning to extract Breast Cancer diagnosis concepts

“…Most of these proposals have focused on the English language [4], [33] and Chinese [16]. Recently there is also growing interest in extracting cancer-related information in other languages such as Italian [34], French [25] and Bulgarian [35].…”