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

• URL: http://arxiv.org/abs/2106.08808v1
• Date: Wed, 16 Jun 2021 14:17:04 GMT
• Title: Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification
• Authors: Benoit Dufumier, Pietro Gori, Julie Victor, Antoine Grigis, Michel Wessa, Paolo Brambilla, Pauline Favre, Mircea Polosan, Colm McDonald, Camille Marie Piguet, Edouard Duchesnay
• Abstract summary: We propose to leverage continuous proxy metadata, in the contrastive learning framework, by introducing a new loss called y-Aware InfoNCE loss. A 3D CNN model pre-trained on $104$ multi-site healthy brain MRI scans can extract relevant features for three classification tasks. When fine-tuned, it also outperforms 3D CNN trained from scratch on these tasks, as well as state-of-the-art self-supervised methods.
• Score: 1.714108629548376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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