MLIP: Medical Language-Image Pre-training with Masked Local Representation Learning

• URL: http://arxiv.org/abs/2401.01591v1
• Date: Wed, 3 Jan 2024 07:54:13 GMT
• Title: MLIP: Medical Language-Image Pre-training with Masked Local Representation Learning
• Authors: Jiarun Liu, Hong-Yu Zhou, Cheng Li, Weijian Huang, Hao Yang, Yong Liang, Shanshan Wang
• Abstract summary: Existing contrastive language-image pre-training aims to learn a joint representation by matching abundant image-text pairs. We propose a Medical Language-Image Pre-training framework, which exploits the limited image-text medical data more efficiently. Our evaluation results show that MLIP outperforms previous work in zero/few-shot classification and few-shot segmentation tasks by a large margin.
• Score: 20.33625985769796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing contrastive language-image pre-training aims to learn a joint representation by matching abundant image-text pairs. However, the number of image-text pairs in medical datasets is usually orders of magnitude smaller than that in natural datasets. Besides, medical image-text pairs often involve numerous complex fine-grained correspondences. This paper aims to enhance the data efficiency by introducing multiple-to-multiple local relationship modeling to capture denser supervisions. More specifically, we propose a Medical Language-Image Pre-training (MLIP) framework, which exploits the limited image-text medical data more efficiently through patch-sentence matching. Furthermore, we introduce a masked contrastive learning strategy with semantic integrity estimation to reduce redundancy in images while preserving the underlying semantics. Our evaluation results show that MLIP outperforms previous work in zero/few-shot classification and few-shot segmentation tasks by a large margin.

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