3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866911873504378880 |
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| author | Emre, Taha Chakravarty, Arunava Rivail, Antoine Lachinov, Dmitrii Leingang, Oliver Riedl, Sophie Mai, Julia Scholl, Hendrik P. N. Sivaprasad, Sobha Rueckert, Daniel Lotery, Andrew Schmidt-Erfurth, Ursula Bogunović, Hrvoje |
| author_facet | Emre, Taha Chakravarty, Arunava Rivail, Antoine Lachinov, Dmitrii Leingang, Oliver Riedl, Sophie Mai, Julia Scholl, Hendrik P. N. Sivaprasad, Sobha Rueckert, Daniel Lotery, Andrew Schmidt-Erfurth, Ursula Bogunović, Hrvoje |
| contents | Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent family of SSL that extract similar representations of two augmented views of an image while pushing away others in the representation space as negatives. However, the state-of-the-art contrastive methods require large batch sizes and augmentations designed for natural images that are impractical for 3D medical images. To address these limitations, we propose a new longitudinal SSL method, 3DTINC, based on non-contrastive learning. It is designed to learn perturbation-invariant features for 3D optical coherence tomography (OCT) volumes, using augmentations specifically designed for OCT. We introduce a new non-contrastive similarity loss term that learns temporal information implicitly from intra-patient scans acquired at different times. Our experiments show that this temporal information is crucial for predicting progression of retinal diseases, such as age-related macular degeneration (AMD). After pretraining with 3DTINC, we evaluated the learned representations and the prognostic models on two large-scale longitudinal datasets of retinal OCTs where we predict the conversion to wet-AMD within a six months interval. Our results demonstrate that each component of our contributions is crucial for learning meaningful representations useful in predicting disease progression from longitudinal volumetric scans. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2312_16980 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | 3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs Emre, Taha Chakravarty, Arunava Rivail, Antoine Lachinov, Dmitrii Leingang, Oliver Riedl, Sophie Mai, Julia Scholl, Hendrik P. N. Sivaprasad, Sobha Rueckert, Daniel Lotery, Andrew Schmidt-Erfurth, Ursula Bogunović, Hrvoje Computer Vision and Pattern Recognition Machine Learning Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent family of SSL that extract similar representations of two augmented views of an image while pushing away others in the representation space as negatives. However, the state-of-the-art contrastive methods require large batch sizes and augmentations designed for natural images that are impractical for 3D medical images. To address these limitations, we propose a new longitudinal SSL method, 3DTINC, based on non-contrastive learning. It is designed to learn perturbation-invariant features for 3D optical coherence tomography (OCT) volumes, using augmentations specifically designed for OCT. We introduce a new non-contrastive similarity loss term that learns temporal information implicitly from intra-patient scans acquired at different times. Our experiments show that this temporal information is crucial for predicting progression of retinal diseases, such as age-related macular degeneration (AMD). After pretraining with 3DTINC, we evaluated the learned representations and the prognostic models on two large-scale longitudinal datasets of retinal OCTs where we predict the conversion to wet-AMD within a six months interval. Our results demonstrate that each component of our contributions is crucial for learning meaningful representations useful in predicting disease progression from longitudinal volumetric scans. |
| title | 3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2312.16980 |