Tianfu Wang

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In this paper, a multiscale convolutional network (MSCN) and graph-partitioning-based method is proposed for accurate segmentation of cervical cytoplasm and nuclei. Specifically, deep learning via the MSCN is explored to extract scale invariant features, and then, segment regions centered at each pixel. The coarse segmentation is refined by an automated(More)
CONTEXT Intimate partner violence has been studied in many developed and developing countries. China remains one of the few large societies for which the prevalence and correlates of intimate partner violence are unknown. METHODS Data from a nationally representative sample of women and men aged 20-64 with a spouse or other steady partner provide(More)
Automatic localization of the standard plane containing complicated anatomical structures in ultrasound (US) videos remains a challenging problem. In this paper, we present a learning-based approach to locate the fetal abdominal standard plane (FASP) in US videos by constructing a domain transferred deep convolutional neural network (CNN). Compared with(More)
Acquisition of the standard plane is the prerequisite of biometric measurement and diagnosis during the ultrasound (US) examination. In this paper, a new algorithm is developed for the automatic recognition of the fetal facial standard planes (FFSPs) such as the axial, coronal, and sagittal planes. Specifically, densely sampled root scale invariant feature(More)
Conventional interpolation algorithms for reconstructing freehand three-dimensional (3D) ultrasound data always contain speckle noises and artifacts. This paper describes a new algorithm for reconstructing regular voxel arrays with reduced speckles and preserved edges. To study speckle statistics properties including mean and variance in sequential B-mode(More)