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Out-of-Distribution Detection in Point-of-Care Ultrasound Breast Imaging Using Variational Autoencoders

Åström, Oskar LU and Karlsson, Jennie LU (2025) In Lecture Notes in Computer Science 15725. p.118-130
Abstract
Out-of-distribution (OOD) detection is a crucial step in ensuring robustness and trustworthiness for deep learning (DL) models. This is particularly important for classification tasks in medical applications. Previous work has shown that point-of-care ultrasound (POCUS) imaging combined with a DL classification network could improve access to early breast cancer diagnosis. In this work, we investigate the ability of a conditional latent space variational autoencoder (CLVAE) to detect OOD samples by learning to cluster in-distribution (ID) data. To efficiently cluster the ID data, three types of feature extraction were used as a preprocessing step to the CLVAE. The three different CLVAE models were evaluated on the task of distinguishing... (More)
Out-of-distribution (OOD) detection is a crucial step in ensuring robustness and trustworthiness for deep learning (DL) models. This is particularly important for classification tasks in medical applications. Previous work has shown that point-of-care ultrasound (POCUS) imaging combined with a DL classification network could improve access to early breast cancer diagnosis. In this work, we investigate the ability of a conditional latent space variational autoencoder (CLVAE) to detect OOD samples by learning to cluster in-distribution (ID) data. To efficiently cluster the ID data, three types of feature extraction were used as a preprocessing step to the CLVAE. The three different CLVAE models were evaluated on the task of distinguishing four different OOD datasets from the ID data, i.e., breast POCUS images. Three of the OOD datasets were designed to resemble real-world OOD scenarios for breast POCUS imaging: (1) ultrasound images of arteries (non-breast); (2) blurry breast POCUS images; (3) POCUS images with contact artifacts. The best CLVAE performance was achieved by extracting features using an existing ultrasound foundation model, yielding an area under the curve (AUC) of 98.7%, 99.8%, and 88.2% for each of the above-mentioned OOD datasets respectively. (Less)
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Image Analysis : 23rd Scandinavian Conference, SCIA 2025, Reykjavik, Iceland, June 23–25, 2025, Proceedings, Part I - 23rd Scandinavian Conference, SCIA 2025, Reykjavik, Iceland, June 23–25, 2025, Proceedings, Part I
series title
Lecture Notes in Computer Science
editor
Petersen, Jens and Andersen Dahl, Vedrana
volume
15725
pages
13 pages
external identifiers
  • scopus:105009801676
ISSN
1611-3349
0302-9743
ISBN
978-3-031-95911-0
978-3-031-95910-3
DOI
10.1007/978-3-031-95911-0_9
language
English
LU publication?
yes
id
79bb04f7-9eb4-4082-8e90-59a39690fe07
date added to LUP
2025-09-24 13:08:08
date last changed
2026-01-23 11:01:01
@inproceedings{79bb04f7-9eb4-4082-8e90-59a39690fe07,
  abstract     = {{Out-of-distribution (OOD) detection is a crucial step in ensuring robustness and trustworthiness for deep learning (DL) models. This is particularly important for classification tasks in medical applications. Previous work has shown that point-of-care ultrasound (POCUS) imaging combined with a DL classification network could improve access to early breast cancer diagnosis. In this work, we investigate the ability of a conditional latent space variational autoencoder (CLVAE) to detect OOD samples by learning to cluster in-distribution (ID) data. To efficiently cluster the ID data, three types of feature extraction were used as a preprocessing step to the CLVAE. The three different CLVAE models were evaluated on the task of distinguishing four different OOD datasets from the ID data, i.e., breast POCUS images. Three of the OOD datasets were designed to resemble real-world OOD scenarios for breast POCUS imaging: (1) ultrasound images of arteries (non-breast); (2) blurry breast POCUS images; (3) POCUS images with contact artifacts. The best CLVAE performance was achieved by extracting features using an existing ultrasound foundation model, yielding an area under the curve (AUC) of 98.7%, 99.8%, and 88.2% for each of the above-mentioned OOD datasets respectively.}},
  author       = {{Åström, Oskar and Karlsson, Jennie}},
  booktitle    = {{Image Analysis : 23rd Scandinavian Conference, SCIA 2025, Reykjavik, Iceland, June 23–25, 2025, Proceedings, Part I}},
  editor       = {{Petersen, Jens and Andersen Dahl, Vedrana}},
  isbn         = {{978-3-031-95911-0}},
  issn         = {{1611-3349}},
  language     = {{eng}},
  month        = {{06}},
  pages        = {{118--130}},
  series       = {{Lecture Notes in Computer Science}},
  title        = {{Out-of-Distribution Detection in Point-of-Care Ultrasound Breast Imaging Using Variational Autoencoders}},
  url          = {{http://dx.doi.org/10.1007/978-3-031-95911-0_9}},
  doi          = {{10.1007/978-3-031-95911-0_9}},
  volume       = {{15725}},
  year         = {{2025}},
}