Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification

Highlights：

• Visualization methods have been developed to identify the impact of input words on the output decision for both deep learning models.

• Models are trained only on Stanford dataset (2512 reports) and are tested on four major healthcare centers dataset – Stanford (1000 reports), Duke (1000 reports), Colorado Children (1000 reports), and University of Pittsburg medical center (858 reports).

• Comparative effectiveness of the deep learning models is judged against the current state-of-the-art – PEFinder as well as with traditional machine learning models – SVM and Adaboost with bag-of-words features.

• This work proposed interesting experimental insight on the proficiency of CNN and RNN to automatize the analysis of unstructured imaging reports.

摘要

•Proposed two distinct deep learning models – (i) CNN Word – Glove, and (ii) Domain phrase attention-based hierarchical neural network (DPA-HNN), for synthesizing information on pulmonary emboli (PE) from clinical thoracic CT free-text radiology reports.•Visualization methods have been developed to identify the impact of input words on the output decision for both deep learning models.•Models are trained only on Stanford dataset (2512 reports) and are tested on four major healthcare centers dataset – Stanford (1000 reports), Duke (1000 reports), Colorado Children (1000 reports), and University of Pittsburg medical center (858 reports).•Comparative effectiveness of the deep learning models is judged against the current state-of-the-art – PEFinder as well as with traditional machine learning models – SVM and Adaboost with bag-of-words features.•This work proposed interesting experimental insight on the proficiency of CNN and RNN to automatize the analysis of unstructured imaging reports.