{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/contrastive-learning-with-continuous-proxy","title":"Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification","arxiv_id":"2106.08808","date":"2021-06-16","proceeding":null,"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":"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.","url_abs":"https://arxiv.org/abs/2106.08808v1","url_pdf":"https://arxiv.org/pdf/2106.08808v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"contrastive-learning-with-continuous-proxy","repo_url":"https://github.com/Duplums/yAwareContrastiveLearning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"contrastive-learning-with-continuous-proxy","repo_url":"https://github.com/guerbet-ai/wsp-contrastive","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"Alzheimer's Detection"},{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"mri-classification","task_name":"MRI classification"}],"methods":[{"method_slug":"3d-cnn","method_name":"3D CNN"},{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"},{"method_slug":"infonce","method_name":"InfoNCE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2106.08808","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}