Latent representation learning for classification of the Doppler ultrasound images

The classification of Doppler ultrasound images plays an important role in the diagnosis of pregnancy. However, it is a challenging problem that suffers from a variable length of these images with a dimension gap between them. In this study, we propose a latent representation weights learning method (LRWL) for pregnancy prediction using Doppler ultrasound images. Unlike most existing methods, LRWL can handle a variable length of multiple images, especially with an irregular multi-image issue. Furthermore, a spatial interaction measurement (SIM) method is proposed to verify the hypothesis that LRWL can more accurately capture relationships among the images. The images, along with diagnostic indices and weights, are integrated as inputs to a deep learning (DL) model for pregnancy prediction. The study conducts comprehensive experiments involving classification tasks on real irregular reproduction datasets and two synthetic regular datasets. Results demonstrate that LRWL surpasses existing methods and is well-suited for irregular multi-image datasets. The proposed method can be effectively implemented using the limited-memory Broyden-Fletcher-Goldfarb-Shanno bound constraint (L-BFGS-B) and the alternating direction minimization (ADM) framework, exhibiting strong performance in terms of accuracy and convergence.