Year: 2018
Type: conference-paper
Abstract: The Impression section of a radiology report summarizes crucial radiology findings in natural language and plays a central role in communicating these findings to physicians.However, the process of generating impressions by summarizing findings is time-consuming for radiologists and prone to errors.We propose to automate the generation of radiology impressions with neural sequence-to-sequence learning.We further propose a customized neural model for this task which learns to encode the study background information and use this information to guide the decoding process.On a large dataset of radiology reports collected from actual hospital studies, our model outperforms existing non-neural and neural baselines under the ROUGE metrics.In a blind experiment, a board-certified radiologist indicated that 67% of sampled system summaries are at least as good as the corresponding humanwritten summaries, suggesting significant clinical validity.To our knowledge our work represents the first attempt in this direction.
DOI: 10.18653/v1/w18-5623
Institution: Stanford University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2891022667