Abstract
Recent advances in medical vision language models guide the learning of visual representations; however, this form of supervision is constrained by the availability of paired image text data, raising the question of whether robust radiology encoders can be learned without relying on language supervision. In this work, we introduce RadJEPA, a self-supervised framework built on a Joint Embedding Predictive Architecture that learns without language supervision. Pre-trained solely on unlabeled chest X-ray images, the model learns to predict latent representations of masked image regions. This predictive objective differs fundamentally from both image text pre-training and DINO-style self-distillation: rather than aligning global representations across views or modalities, RadJEPA explicitly models latent-space prediction. We evaluate the learned encoder on disease classification, semantic segmentation, and report generation tasks. Across benchmarks, RadJEPA achieves performance exceeding state-of-the-art approaches, including Rad-DINO.
Abstract (translated)
近期的医学视觉语言模型的发展指导了视觉表示的学习；然而，这种形式的监督受限于配对图像文本数据的可用性，引发了是否可以不依赖语言监督来学习稳健的放射学编码器的问题。在本工作中，我们介绍了RadJEPA，这是一种基于联合嵌入预测架构构建的自监督框架，它可以在没有语言监督的情况下进行学习。该模型仅通过未标记的胸部X光图像预训练，学习对遮蔽图像区域的潜在表示进行预测。这种预测目标与图像文本预训练和DINO风格的自我蒸馏方法根本不同：RadJEPA不是跨视图或模态对齐全局表示，而是明确地建模潜在空间中的预测。 我们在疾病分类、语义分割和报告生成任务上评估了所学习到的编码器。在各个基准测试中，RadJEPA的表现超过了包括Rad-DINO在内的最先进方法。
URL
https://arxiv.org/abs/2601.15891
