# Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification Source: [Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:07:38.365Z Authors: Benoit Dufumier, Pietro Gori, Julie Victor, Antoine Grigis, Michel Wessa, Paolo Brambilla, Pauline Favre, Mircea Polosan, Colm McDonald, Camille Marie Piguet, Edouard Duchesnay Published: 2021-06-16T14:17:04.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Cirrhosis Classification on D\_histo^{1+2} (N=155)](): Cirrhosis Classification; D\_histo^{1+2} (N=155); AUC: 0.8. - [Cirrhosis Classification on D\_histo](): Cirrhosis Classification; D\_histo; AUC: 0.83. - [AD Conversion Prediction on ADNI 3-years window](): AD conversion prediction; ADNI 3-years window; AUROC: 71.6. - [AD conversion prediction on ADNI (1-year window)](): AD conversion prediction; ADNI (1-year window); AUROC: 0.73. - [Cirrhosis Classification on D\_histo^2 N=49](): Cirrhosis Classification; D\_histo^2 N=49; AUC: 0.82. ## Original sources and implementation [Original paper]() [PDF]()