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• W2891288863 abstract "We present a novel multi-task convolutional neural network called Multi-task SonoEyeNet (M-SEN) that learns to generate clinically relevant visual attention maps using sonographer gaze tracking data on input ultrasound (US) video frames so as to assist standardized abdominal circumference (AC) plane detection. Our architecture consists of a generator and a discriminator, which are trained in an adversarial scheme. The generator learns sonographer attention on a given US video frame to predict the frame label (standardized AC plane/background). The discriminator further fine-tunes the predicted attention map by encouraging it to mimick the ground-truth sonographer attention map. The novel model expands the potential clinical usefulness of a previous model by eliminating the requirement of input gaze tracking data during inference without compromising its plane detection performance (Precision: 96.8, Recall: 96.2, F-1 score: 96.5)." @default.
• W2891288863 created "2018-09-27" @default.
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• W2891288863 date "2018-01-01" @default.
• W2891288863 modified "2023-10-16" @default.
• W2891288863 title "Multi-task SonoEyeNet: Detection of Fetal Standardized Planes Assisted by Generated Sonographer Attention Maps" @default.
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• W2891288863 doi "https://doi.org/10.1007/978-3-030-00928-1_98" @default.
• W2891288863 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6459365" @default.
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• W2891288863 hasPublicationYear "2018" @default.
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