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

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.

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution燤odel

“…Why there hasn't been enough study on how to discover lung cancer nodules that have been divided into segments is explained in Table 1. Researchers from [11][12][13][14] used the identical data set, but the model's robustness was degraded. Because the U-NET could not be utilized with new data types, the IOU intersection and dice coefficient index accuracy were unavailable.…”