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

研究成果: 雜誌貢獻文章同行評審

摘要

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.

原文英語
文章編號417
期刊Journal of Personalized Medicine
12
發行號3
DOIs
出版狀態已發佈 - 3月 2022

ASJC Scopus subject areas

  • 醫藥(雜項)

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