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- W4385261736 abstract "Contrastive learning based vision-language joint pre-training has emerged as a successful representation learning strategy. In this paper, we present a prototype representation learning framework incorporating both global and local alignment between medical images and reports. In contrast to standard global multi-modality alignment methods, we employ a local alignment module for fine-grained representation. Furthermore, a cross-modality conditional reconstruction module is designed to interchange information across modalities in the training phase by reconstructing masked images and reports. For reconstructing long reports, a sentence-wise prototype memory bank is constructed, enabling the network to focus on low-level localized visual and high-level clinical linguistic features. Additionally, a non-auto-regressive generation paradigm is proposed for reconstructing non-sequential reports. Experimental results on five downstream tasks, including supervised classification, zero-shot classification, image-to-text retrieval, semantic segmentation, and object detection, show the proposed method outperforms other state-of-the-art methods across multiple datasets and under different dataset size settings. The code is available at https://github.com/QtacierP/PRIOR." @default.
- W4385261736 created "2023-07-26" @default.
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- W4385261736 date "2023-07-24" @default.
- W4385261736 modified "2023-09-30" @default.
- W4385261736 title "PRIOR: Prototype Representation Joint Learning from Medical Images and Reports" @default.
- W4385261736 doi "https://doi.org/10.48550/arxiv.2307.12577" @default.
- W4385261736 hasPublicationYear "2023" @default.
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