Riqiang Gao, Yuankai Huo, Shunxing Bao, Yucheng Tang, Sanja L Antic, Emily S Epstein, Aneri B Balar, Steve Deppen, Alexis B Paulson, Kim L Sandler, Pierre P Massion, Bennett A Landman
The field of lung nodule detection and cancer prediction has been rapidly developing with the support of large public data archives. Previous studies have largely focused cross-sectional (single) CT data. Herein, we consider longitudinal data. The Long Short-Term Memory (LSTM) model addresses learning with regularly spaced time points (i.e., equal temporal intervals). However, clinical imaging follows patient needs with often heterogeneous, irregular acquisitions. To model both regular and irregular longitudinal samples, we generalize the LSTM model with the Distanced LSTM (DLSTM) for temporally varied acquisitions...
October 2019: Machine Learning in Medical Imaging