Study on the Evolution and Diagnostic Model of Early Stage Lung Cancer
the underlying mechanism for early identification of lung cancer. This study is expected to provide new technologies for large-scale early stage screening of lung cancer, which has important academic value and clinical value.
The study on tracking and extraction of cross-period sequences of pulmonary lesions in long-term CT images, and to explore a deep learning method for lung cancer staging in which spatial multivariate features (deep and non-deep features) are combined under the key attributes of medical records; the study on the dependent relationship of the 3D feature of lesions on the time dimension, and to explore the method of accurately tracing early sequence images of lung cancer in middle and late stage sequence images and the modeling mechanism of early lesion characteristics over time; the study on the relationship between long-term CT sequence image data and diagnostic information features
including time series and disease stages, and to explore the effectiveness of LSTM depth framework under time-incomplete data.
Yan Qiang, Taiyuan University of Technology
Juanjuan Zhao, Taiyuan University of Technology
Rui Hao, Shanxi University of Finance and Economics
MSTS-Net: malignancy evolution prediction of pulmonary nodules from longitudinal CT images via multi-task spatial-temporal self-attention network.
Song P, Hou J, Xiao N, Zhao J, Zhao J, Qiang Y, Yang Q
Int J Comput Assist Radiol Surg. 2022 Nov 29 PUBMED