Deep Multi-Objective Learning from Low-Dose CT for Automatic Lung-RADS Report Generation

Yung Chun Chang, Yan Chun Hsing, Yu Wen Chiu, Cho Chiang Shih, Jun Hong Lin, Shih Hsin Hsiao, Koji Sakai, Kai Hsiung Ko, Cheng Yu Chen

Research output: Contribution to journalArticlepeer-review

Abstract

Radiology report generation through chest radiography interpretation is a time-consuming task that involves the interpretation of images by expert radiologists. It is common for fatigue-induced diagnostic error to occur, and especially difficult in areas of the world where radiologists are not available or lack diagnostic expertise. In this research, we proposed a multi-objective deep learning model called CT2Rep (Computed Tomography to Report) for generating lung radiology reports by extracting semantic features from lung CT scans. A total of 458 CT scans were used in this research, from which 107 radiomics features and 6 slices of segmentation related nodule features were extracted for the input of our model. The CT2Rep can simultaneously predict position, margin, and texture, which are three important indicators of lung cancer, and achieves remarkable performance with an F1-score of 87.29%. We conducted a satisfaction survey for estimating the practicality of CT2Rep, and the results show that 95% of the reports received satisfactory ratings. The results demonstrate the great potential in this model for the production of robust and reliable quantitative lung diagnosis reports. Medical personnel can obtain important indicators simply by providing the lung CT scan to the system, which can bring about the widespread application of the proposed framework.

Original languageEnglish
Article number417
JournalJournal of Personalized Medicine
Volume12
Issue number3
DOIs
Publication statusPublished - Mar 2022

Keywords

  • Automatic radiology report generation
  • Deep neural network
  • Medical informatics
  • Natural language processing

ASJC Scopus subject areas

  • Medicine (miscellaneous)

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