Standard
Instance-Specific Augmentation of Brain MRIs with Variational Autoencoders. / Middleton, Jon Anthony; Bauer, Marko; Johansen, Jacob; Nielsen, Mads; Sommer, Stefan Horst; Pai, Akshay Sadananda Uppinakudru.
Medical Applications with Disentanglements : First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Springer, 2023. s. 49-58 (Lecture Notes in Computer Science, Bind 13823).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Middleton, JA, Bauer, M, Johansen, J, Nielsen, M, Sommer, SH & Pai, ASU 2023,
Instance-Specific Augmentation of Brain MRIs with Variational Autoencoders. i
Medical Applications with Disentanglements : First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Springer, Lecture Notes in Computer Science, bind 13823, s. 49-58, First International Workshop, MILLanD 2022, Singapore,
22/09/2022.
https://doi.org/10.1007/978-3-031-25046-0_5
APA
Middleton, J. A., Bauer, M., Johansen, J., Nielsen, M., Sommer, S. H., & Pai, A. S. U. (2023).
Instance-Specific Augmentation of Brain MRIs with Variational Autoencoders. I
Medical Applications with Disentanglements : First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings (s. 49-58). Springer. Lecture Notes in Computer Science Bind 13823
https://doi.org/10.1007/978-3-031-25046-0_5
Vancouver
Middleton JA, Bauer M, Johansen J, Nielsen M, Sommer SH, Pai ASU.
Instance-Specific Augmentation of Brain MRIs with Variational Autoencoders. I Medical Applications with Disentanglements : First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Springer. 2023. s. 49-58. (Lecture Notes in Computer Science, Bind 13823).
https://doi.org/10.1007/978-3-031-25046-0_5
Author
Middleton, Jon Anthony ; Bauer, Marko ; Johansen, Jacob ; Nielsen, Mads ; Sommer, Stefan Horst ; Pai, Akshay Sadananda Uppinakudru. / Instance-Specific Augmentation of Brain MRIs with Variational Autoencoders. Medical Applications with Disentanglements : First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Springer, 2023. s. 49-58 (Lecture Notes in Computer Science, Bind 13823).
Bibtex
@inproceedings{58726862d1514d97b32f2e5ac69b568f,
title = "Instance-Specific Augmentation of Brain MRIs with Variational Autoencoders",
abstract = "Spatial data augmentation is a standard technique for regularizing deep segmentation networks that are tasked with localizing medical abnormalities. However, a typical spatial augmentation scheme is built upon ad hoc selections of spatial transformation parameters which are not determined by the data set and therefore may not capture spatial variations in the data. For segmentation networks trained in the low-data regime, these ad hoc transformation techniques often fail to encourage better generalization. To address this problem, we propose a variational autoencoder framework for spatial data augmentation. We show how this framework provides a natural, data-driven approach to probabilistic, instance-specific spatial augmentation. Further, we observe that U-Nets trained on data augmented using this framework compare favorably with U-Nets trained using standard spatial augmentation methods.",
author = "Middleton, {Jon Anthony} and Marko Bauer and Jacob Johansen and Mads Nielsen and Sommer, {Stefan Horst} and Pai, {Akshay Sadananda Uppinakudru}",
year = "2023",
doi = "10.1007/978-3-031-25046-0_5",
language = "English",
isbn = "978-3-031-25045-3",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "49--58",
booktitle = "Medical Applications with Disentanglements",
address = "Switzerland",
note = "First International Workshop, MILLanD 2022 : [Held in Conjunction with MICCAI 2022] ; Conference date: 22-09-2022",
}
RIS
TY - GEN
T1 - Instance-Specific Augmentation of Brain MRIs with Variational Autoencoders
AU - Middleton, Jon Anthony
AU - Bauer, Marko
AU - Johansen, Jacob
AU - Nielsen, Mads
AU - Sommer, Stefan Horst
AU - Pai, Akshay Sadananda Uppinakudru
PY - 2023
Y1 - 2023
N2 - Spatial data augmentation is a standard technique for regularizing deep segmentation networks that are tasked with localizing medical abnormalities. However, a typical spatial augmentation scheme is built upon ad hoc selections of spatial transformation parameters which are not determined by the data set and therefore may not capture spatial variations in the data. For segmentation networks trained in the low-data regime, these ad hoc transformation techniques often fail to encourage better generalization. To address this problem, we propose a variational autoencoder framework for spatial data augmentation. We show how this framework provides a natural, data-driven approach to probabilistic, instance-specific spatial augmentation. Further, we observe that U-Nets trained on data augmented using this framework compare favorably with U-Nets trained using standard spatial augmentation methods.
AB - Spatial data augmentation is a standard technique for regularizing deep segmentation networks that are tasked with localizing medical abnormalities. However, a typical spatial augmentation scheme is built upon ad hoc selections of spatial transformation parameters which are not determined by the data set and therefore may not capture spatial variations in the data. For segmentation networks trained in the low-data regime, these ad hoc transformation techniques often fail to encourage better generalization. To address this problem, we propose a variational autoencoder framework for spatial data augmentation. We show how this framework provides a natural, data-driven approach to probabilistic, instance-specific spatial augmentation. Further, we observe that U-Nets trained on data augmented using this framework compare favorably with U-Nets trained using standard spatial augmentation methods.
U2 - 10.1007/978-3-031-25046-0_5
DO - 10.1007/978-3-031-25046-0_5
M3 - Article in proceedings
SN - 978-3-031-25045-3
T3 - Lecture Notes in Computer Science
SP - 49
EP - 58
BT - Medical Applications with Disentanglements
PB - Springer
T2 - First International Workshop, MILLanD 2022
Y2 - 22 September 2022
ER -
