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A Dual Adversarial Calibration Framework for Automatic Fetal Brain Biometry by Gao, Yuan; Lee, Lok Hin; Droste, Richard; Craik, Rachel; Beriwal, Sridevi; Papageorghiou, Aris; Noble, Alison is a scholarly article available to read on EtoBox.

What is A Dual Adversarial Calibration Framework for Automatic Fetal Brain Biometry about?

This paper presents a novel approach to automatic fetal brain biometry motivated by needs in low- and medium- income countries. Specifically, we leverage high-end (HE) ultrasound images to build a biometry solution for low-cost (LC) point-of-care ultrasound images. We propose a novel unsupervised domain adaptation approach to train deep models to be invariant to significant image distribution shift between the image types. Our proposed method, which employs a Dual Adversarial Calibration (DAC) framework, consists of adversarial pathways which enforce model invariance to; i) adversarial perturbations in the feature space derived from LC images, and ii) appearance domain discrepancy. Our Dual Adversarial Calibration method estimates transcerebellar diameter and head circumference on images from low-cost ultrasound devices with a mean absolute error (MAE) of 2.43mm and 1.65mm, compared with 7.28 mm and 5.65 mm respectively for SOTA.

Author

Gao, Yuan; Lee, Lok Hin; Droste, Richard; Craik, Rachel; Beriwal, Sridevi; Papageorghiou, Aris; Noble, Alison

Published

2021

Language

EN