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arXiv Machine Learning By Michelle Espranita Liman, Ioana Ionac, Alexander M\"uller, Eimo Martens, Daniel Rueckert, Philip M\"uller

EchoTab: Learning Measurement-Grounded Representations for Echocardiographic Diagnosis through Tabular Reconstruction

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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