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Contrastive Learning with Continuous Proxy Meta-data for 3D MRI Classification

2021; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-030-87196-3_6

1611-3349

Benoît Dufumier, Pietro Gori, Julie Victor, Antoine Grigis, Michèle Wessa, Paolo Brambilla, Pauline Favre, Mircea Polosan, Colm McDonald, Camille Piguet, Mary Phillips, Lisa T. Eyler, Édouard Duchesnay,

Machine Learning in Healthcare

Traditional supervised learning with deep neural networks requires a tremendous amount of labelled data to converge to a good solution. For 3D medical images, it is often impractical to build a large homogeneous annotated dataset for a specific pathology. Self-supervised methods offer a new way to learn a representation of the images in an unsupervised manner with a neural network. In particular, contrastive learning has shown great promises by (almost) matching the performance of fully-supervised CNN on vision tasks. Nonetheless, this method does not take advantage of available meta-data, such as participant's age, viewed as prior knowledge. Here, we propose to leverage continuous proxy metadata, in the contrastive learning framework, by introducing a new loss called y-Aware InfoNCE loss. Specifically, we improve the positive sampling during pre-training by adding more positive examples with similar proxy meta-data with the anchor, assuming they share similar discriminative semantic features. With our method, a 3D CNN model pre-trained on \(10^4\) multi-site healthy brain MRI scans can extract relevant features for three classification tasks: schizophrenia, bipolar diagnosis and Alzheimer's detection. When fine-tuned, it also outperforms 3D CNN trained from scratch on these tasks, as well as state-of-the-art self-supervised methods. Our code is made publicly available here.