Transfer Learning with a Layer Dependent Regularization for Medical Image Segmentation

Nimrod Sagie; Hayit Greenspan; Jacob Goldberger

doi:https://doi.org/10.1007/978-3-030-87589-3_17

Transfer Learning with a Layer Dependent Regularization for Medical Image Segmentation

Nimrod Sagie, Hayit Greenspan, Jacob Goldberger

Tel Aviv University, Bar-Ilan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Transfer learning is a machine learning technique where a model trained on one task is used to initialize the learning procedure of a second related task which has only a small amount of training data. Transfer learning can also be used as a regularization procedure by penalizing the learned parameters if they deviate too much from their initial values. In this study we show that the learned parameters move apart from the source task as the image processing progresses along the network layers. To cope with this behaviour we propose a transfer regularization method based on monotonically decreasing regularization coefficients. We demonstrate the power of the proposed regularized transfer learning scheme on COVID-19 opacity task. Specifically, we show that it can improve the segmentation of coronavirus lesions in chest CT scans.

Original language	English
Title of host publication	Machine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Proceedings
Editors	Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Yan
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	161-170
Number of pages	10
ISBN (Print)	9783030875886
DOIs	https://doi.org/10.1007/978-3-030-87589-3_17
State	Published - 2021
Event	12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online Duration: 27 Sep 2021 → 27 Sep 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12966 LNCS

Conference

Conference	12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
City	Virtual, Online
Period	27/09/21 → 27/09/21

Keywords

COVID-19 opacity
Regularization
Transfer learning

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

https://doi.org/10.1007/978-3-030-87589-3_17

Cite this

Sagie, N., Greenspan, H., & Goldberger, J. (2021). Transfer Learning with a Layer Dependent Regularization for Medical Image Segmentation. In C. Lian, X. Cao, I. Rekik, X. Xu, & P. Yan (Eds.), Machine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Proceedings (pp. 161-170). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12966 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-87589-3_17

Sagie, Nimrod ; Greenspan, Hayit ; Goldberger, Jacob. / Transfer Learning with a Layer Dependent Regularization for Medical Image Segmentation. Machine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Proceedings. editor / Chunfeng Lian ; Xiaohuan Cao ; Islem Rekik ; Xuanang Xu ; Pingkun Yan. Springer Science and Business Media Deutschland GmbH, 2021. pp. 161-170 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Transfer learning is a machine learning technique where a model trained on one task is used to initialize the learning procedure of a second related task which has only a small amount of training data. Transfer learning can also be used as a regularization procedure by penalizing the learned parameters if they deviate too much from their initial values. In this study we show that the learned parameters move apart from the source task as the image processing progresses along the network layers. To cope with this behaviour we propose a transfer regularization method based on monotonically decreasing regularization coefficients. We demonstrate the power of the proposed regularized transfer learning scheme on COVID-19 opacity task. Specifically, we show that it can improve the segmentation of coronavirus lesions in chest CT scans.",

keywords = "COVID-19 opacity, Regularization, Transfer learning",

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doi = "https://doi.org/10.1007/978-3-030-87589-3_17",

language = "الإنجليزيّة",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "161--170",

editor = "Chunfeng Lian and Xiaohuan Cao and Islem Rekik and Xuanang Xu and Pingkun Yan",

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Sagie, N, Greenspan, H & Goldberger, J 2021, Transfer Learning with a Layer Dependent Regularization for Medical Image Segmentation. in C Lian, X Cao, I Rekik, X Xu & P Yan (eds), Machine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12966 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 161-170, 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online, 27/09/21. https://doi.org/10.1007/978-3-030-87589-3_17

Transfer Learning with a Layer Dependent Regularization for Medical Image Segmentation. / Sagie, Nimrod; Greenspan, Hayit ; Goldberger, Jacob.
Machine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Proceedings. ed. / Chunfeng Lian; Xiaohuan Cao; Islem Rekik; Xuanang Xu; Pingkun Yan. Springer Science and Business Media Deutschland GmbH, 2021. p. 161-170 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12966 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Transfer Learning with a Layer Dependent Regularization for Medical Image Segmentation

AU - Sagie, Nimrod

AU - Greenspan, Hayit

AU - Goldberger, Jacob

PY - 2021

Y1 - 2021

N2 - Transfer learning is a machine learning technique where a model trained on one task is used to initialize the learning procedure of a second related task which has only a small amount of training data. Transfer learning can also be used as a regularization procedure by penalizing the learned parameters if they deviate too much from their initial values. In this study we show that the learned parameters move apart from the source task as the image processing progresses along the network layers. To cope with this behaviour we propose a transfer regularization method based on monotonically decreasing regularization coefficients. We demonstrate the power of the proposed regularized transfer learning scheme on COVID-19 opacity task. Specifically, we show that it can improve the segmentation of coronavirus lesions in chest CT scans.

AB - Transfer learning is a machine learning technique where a model trained on one task is used to initialize the learning procedure of a second related task which has only a small amount of training data. Transfer learning can also be used as a regularization procedure by penalizing the learned parameters if they deviate too much from their initial values. In this study we show that the learned parameters move apart from the source task as the image processing progresses along the network layers. To cope with this behaviour we propose a transfer regularization method based on monotonically decreasing regularization coefficients. We demonstrate the power of the proposed regularized transfer learning scheme on COVID-19 opacity task. Specifically, we show that it can improve the segmentation of coronavirus lesions in chest CT scans.

KW - COVID-19 opacity

KW - Regularization

KW - Transfer learning

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U2 - https://doi.org/10.1007/978-3-030-87589-3_17

DO - https://doi.org/10.1007/978-3-030-87589-3_17

M3 - منشور من مؤتمر

SN - 9783030875886

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 161

EP - 170

BT - Machine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Proceedings

A2 - Lian, Chunfeng

A2 - Cao, Xiaohuan

A2 - Rekik, Islem

A2 - Xu, Xuanang

A2 - Yan, Pingkun

PB - Springer Science and Business Media Deutschland GmbH

T2 - 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021

Y2 - 27 September 2021 through 27 September 2021

ER -

Sagie N, Greenspan H , Goldberger J. Transfer Learning with a Layer Dependent Regularization for Medical Image Segmentation. In Lian C, Cao X, Rekik I, Xu X, Yan P, editors, Machine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 161-170. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: https://doi.org/10.1007/978-3-030-87589-3_17

Transfer Learning with a Layer Dependent Regularization for Medical Image Segmentation

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Keywords

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