Proposes TinyUSFM-uLPIPS and TinyUSFM-NRQ metrics that show better alignment with segmentation task performance and expert preference than PSNR or VGG-LPIPS in ultrasound imaging.
USFM : A universal ultrasound foundation model generalized to tasks and organs towards label efficient image analysis
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
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.
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.
Systematic tests of 27 ultrasound tasks show that unified training is more consistent than clinically-grouped training, with performance hinging on data availability and task characteristics.
citing papers explorer
-
Defining Robust Ultrasound Quality Metrics via an Ultrasound Foundation Model
Proposes TinyUSFM-uLPIPS and TinyUSFM-NRQ metrics that show better alignment with segmentation task performance and expert preference than PSNR or VGG-LPIPS in ultrasound imaging.
-
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.
-
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.
-
Understanding Task Aggregation for Generalizable Ultrasound Foundation Models
Systematic tests of 27 ultrasound tasks show that unified training is more consistent than clinically-grouped training, with performance hinging on data availability and task characteristics.