2022-12-31-ryser22a.md

abstract	booktitle	title	volume	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	genre	issued	pdf	extras
We propose a novel anomaly detection method for echocardiogram videos. The introduced method takes advantage of the periodic nature of the heart cycle to learn three variants of a variational latent trajectory model (TVAE). While the first two variants (TVAE-C and TVAE-R) model strict periodic movements of the heart, the third (TVAE-S) is more general and allows shifts in the spatial representation throughout the video. All models are trained on the healthy samples of a novel in-house dataset of infant echocardiogram videos consisting of multiple chamber views to learn a normative prior of the healthy population. During inference, maximum a posteriori (MAP) based anomaly detection is performed to detect out-of-distribution samples in our dataset. The proposed method reliably identifies severe congenital heart defects, such as Ebstein’s Anomaly or Shone-complex. Moreover, it achieves superior performance over MAP-based anomaly detection with standard variational autoencoders when detecting pulmonary hypertension and right ventricular dilation. Finally, we demonstrate that the proposed method enables interpretable explanations of its output through heatmaps highlighting the regions corresponding to anomalous heart structures.	Proceedings of the 7th Machine Learning for Healthcare Conference	Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models	182	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	ryser22a	0	Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models	425	458	425-458	425	false	Ryser, Alain and Manduchi, Laura and Laumer, Fabian and Michel, Holger and Wellmann, Sven and Vogt, Julia E.	given family Alain Ryser given family Laura Manduchi given family Fabian Laumer given family Holger Michel given family Sven Wellmann given family Julia E. Vogt	2022-12-31		Proceedings of the 7th Machine Learning for Healthcare Conference	inproceedings	date-parts 2022 12 31	https://proceedings.mlr.press/v182/ryser22a/ryser22a.pdf