# Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels Source: [Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels]() Collection index: [llms.txt]() Data updated: 2025-12-24T16:00:25.815Z Authors: Jizong Peng, Ping Wang, Chrisitian Desrosiers, Marco Pedersoli Published: 2021-07-29T04:30:46.000Z ## Abstract Pre-training a recognition model with contrastive learning on a large dataset of unlabeled data has shown great potential to boost the performance of a downstream task, e.g., image classification. However, in domains such as medical imaging, collecting unlabeled data can be challenging and expensive. In this work, we propose to adapt contrastive learning to work with meta-label annotations, for improving the model's performance in medical image segmentation even when no additional unlabeled data is available. Meta-labels such as the location of a 2D slice in a 3D MRI scan or the type of device used, often come for free during the acquisition process. We use the meta-labels for pre-training the image encoder as well as to regularize a semi-supervised training, in which a reduced set of annotated data is used for training. Finally, to fully exploit the weak annotations, a self-paced learning approach is used to help the learning and discriminate useful labels from noise. Results on three different medical image segmentation datasets show that our approach: i) highly boosts the performance of a model trained on a few scans, ii) outperforms previous contrastive and semi-supervised approaches, and iii) reaches close to the performance of a model trained on the full data. ## 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. - [Medical Image Segmentation on PROMISE12](): Medical Image Segmentation; PROMISE12; Dice Coefficient: 74.02. ## Original sources and implementation [Original paper]() [PDF]()