Fetal heart ultrasound image-oriented adaptive classification deep model based on differentiable architecture search

Abstract Prenatal ultrasound examination is used for screening congenital heart defects and fetal genetic diseases. Unfavorable factors such as low signal-to-noise ratio, artifact and poor fetal posture in ultrasound images make it a very complicated task to identify and interpret the standard scan plane of the fetal heart in prenatal ultrasound examinations. Deep learning related methods are widely used to process and analyze medical images. However, designing an effective network structure for a specific task is a time-consuming and relies on expert knowledge. In order to obtain an effective fetal ultrasound image classification model in a short time, this paper collects and organizes the Fetal Heart Standard Plane(FHSP) level III screening dataset, and we use the Differentiable Architecture Search(DARTS) method for FHSP classification task to automatically obtain an efficient adaptive classification deep model called Ultrasound Image Adaptive Classification model(UIAC) for assisting the diagnosis of fetal congenital heart disease. This new model is a deep neural network consisting of two automatically searched optimal blocks. Our UIAC model has fewer parameters than the mainstream manned classification networks. Moreover, it has achieved the best recognition results on the FHSP classification task: top1-accuracy 89.84%, macro-f1 89.72%, kappa score 88.82%.