medical processing

The classification of Doppler ultrasound imagesis very important for conception prediction. However it is achallenging problem that suffers from a variable length of thoseimages with a dimension gap between them. In this study, wepropose a latent representation weight learning method (LRWL)for conception prediction with Doppler ultrasound images. Unlikemost existing related methods, LRWL can process a variablelength of multiple images, particularly with an irregular multi-image issue. LRWL can extract the relation between the imagesand then learn the latent representative weight of each image.Comparatively, we also propose another method—spatiotemporalinteraction measurement (SIM), to validate the experimentalassumption that LRWL can describe the role of each image moreaccurately. Then we integrate the images with the weights andthe diagnostic indices, as the input data to a deep learning (DL)model to predict the successful conception. Finally, we conductthe comprehensive experiments with the classification tasks onthe real irregular reproduction datasets and two other syntheticregular datasets. Experimental results show that the proposedLRWL outperforms existing relative methods and is suitable forthe irregular multi-image datasets. It can be efficiently optimizedunder the limited-memory Broyden-Fletcher-Goldfarb-Shannobound constraint algorithm (L-BFGS-B), and the alternatingdirection minimization (ADM) framework. Thus, the proposedmethod can achieve the good performance with high accuracyand good convergence.