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Contrastive learning based vision-language joint pre-training has emerged asa successful representation learning strategy. In this paper, we present aprototype representation learning framework incorporating both global and localalignment between medical images and reports. In contrast to standard globalmulti-modality alignment methods, we employ a local alignment module forfine-grained representation. Furthermore, a cross-modality conditionalreconstruction module is designed to interchange information across modalitiesin the training phase by reconstructing masked images and reports. Forreconstructing long reports, a sentence-wise prototype memory bank isconstructed, enabling the network to focus on low-level localized visual andhigh-level clinical linguistic features. Additionally, a non-auto-regressivegeneration paradigm is proposed for reconstructing non-sequential reports.Experimental results on five downstream tasks, including supervisedclassification, zero-shot classification, image-to-text retrieval, semanticsegmentation, and object detection, show the proposed method outperforms otherstate-of-the-art methods across multiple datasets and under different datasetsize settings. The code is available at https://github.com/QtacierP/PRIOR.

PRIOR: Prototype Representation Joint Learning from Medical Images and Reports