arXiv Computer Vision
EchoXFlow: A Beamspace Echocardiography Dataset for Cardiac Motion, Flow, and Function
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arXiv Computer Vision
Oct 9
EchoDiST: Self-distillation-based joint learning for diffusion-conditioned echocardiographic myocardial motion estimation
EchoDiST is an unsupervised framework for estimating myocardial motion in echocardiography, combining self‑distillation joint learning of anatomical segmentation and motion estimation with a diffusion‑conditioned network. The method uses limited anatomical annotations and stochastic perturbations during training, yet requires only a single deterministic forward pass at inference. Evaluated on three datasets, EchoDiST consistently outperforms seven other learning‑based approaches in anatomical alignment, strain assessment, and functional metrics, with statistically significant improvements across cross‑view and cross‑dataset tests.
Hugging Face Trending Papers
Oct 8
EchoDiST: Self-distillation-based joint learning for diffusion-conditioned echocardiographic myocardial motion estimation
EchoDiST is an unsupervised framework for estimating myocardial motion in echocardiography. It combines self‑distillation joint learning of anatomical segmentation and motion estimation with a diffusion‑conditioned network, requiring only limited anatomical annotations. The method achieves statistically significant improvements in anatomical alignment, strain assessment, and functional metrics across three datasets, outperforming seven other learning‑based approaches.
arXiv Machine Learning
Oct 12
EchoTab: Learning Measurement-Grounded Representations for Echocardiographic Diagnosis through Tabular Reconstruction
EchoTab is a representation learning framework for multi‑view echocardiography that uses measurement‑specific tokens as an intermediate representation between echo views and diagnosis. Each token attends to the views relevant to its measurement and is trained via tabular reconstruction to predict the measurement value, producing a factorized representation that encodes clinically meaningful structural and functional properties. Experiments on two datasets show that EchoTab improves diagnosis classification and that its learned attention aligns with cardiologists’ established measurement‑view relationships.
arXiv Computer Vision
Oct 8
EchoDino: A pediatric foundation model for transferable echocardiographic analysis across the lifespan
arXiv:2610.05603v2 Announce Type: replace Abstract: Echocardiography is the most widely used cardiac imaging modality, yet interpretation demands integrating visual evidence across global anatomy, lo...
Hugging Face Trending Papers
Sep 2
Learning from Scarce Labels: Multi-View Echocardiography for Ejection Fraction Prediction
The paper introduces the first publicly available dataset for predicting left ventricular ejection fraction (EF) from parasternal long-axis (PLAX) echocardiography, comprising over 25,000 labeled videos generated through a novel data‑generation strategy that correlates clinical notes with echocardiographic videos. Using this dataset, the authors train a reproducible PLAX‑EF model that achieves a mean absolute error (MAE) of 6.86%, comparable to the clinical standard of apical four‑chamber (A4C) methods. They further show that combining PLAX and A4C predictions via simple late fusion reduces MAE to 6.37%, highlighting the benefit of multi‑view integration, and they release the dataset, models, and demos for community use.
arXiv Machine Learning
Sep 4
Learning from Scarce Labels: Multi-View Echocardiography for Ejection Fraction Prediction
The paper introduces the first publicly available dataset of over 25,000 parasternal long‑axis (PLAX) echocardiography videos labeled for left ventricular ejection fraction (EF), created through a novel data‑generation strategy that links clinical notes to video data. Using this dataset, the authors train a reproducible PLAX‑based EF model that achieves a mean absolute error (MAE) of 6.86%, comparable to the clinical standard of apical four‑chamber (A4C) methods. They further show that simple late fusion of PLAX and A4C predictions reduces MAE to 6.37%, highlighting the benefit of multi‑view integration, and release the dataset, models, and demos publicly.