PolarMAE is a new unsupervised pre-training method for fetal ultrasound that uses progressive visual-semantic screening, acoustic-bounded constraints, and polar-texture masking to reach state-of-the-art performance on downstream interpretation tasks.
Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image Recognition , July 2023
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
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UNVERDICTED 2representative citing papers
LAMAE adds latent-space attention to masked autoencoders so multi-view echocardiography videos can exchange information across frames and views, yielding representations that transfer from adult to pediatric hearts and enable ICD-10 code prediction on MIMIC-IV-ECHO.
citing papers explorer
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PolarMAE: Efficient Fetal Ultrasound Pre-training via Semantic Screening and Polar-Guided Masking
PolarMAE is a new unsupervised pre-training method for fetal ultrasound that uses progressive visual-semantic screening, acoustic-bounded constraints, and polar-texture masking to reach state-of-the-art performance on downstream interpretation tasks.
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Beyond Independent Frames: Latent Attention Masked Autoencoders for Multi-View Echocardiography
LAMAE adds latent-space attention to masked autoencoders so multi-view echocardiography videos can exchange information across frames and views, yielding representations that transfer from adult to pediatric hearts and enable ICD-10 code prediction on MIMIC-IV-ECHO.