UniDCP: Unifying Multiple Medical Vision-language Tasks via Dynamic Cross-modal Learnable Prompts

• URL: http://arxiv.org/abs/2312.11171v1
• Date: Mon, 18 Dec 2023 13:18:24 GMT
• Title: UniDCP: Unifying Multiple Medical Vision-language Tasks via Dynamic Cross-modal Learnable Prompts
• Authors: Chenlu Zhan, Yufei Zhang, Yu Lin, Gaoang Wang, Hongwei Wang
• Abstract summary: We propose UniDCP, a Unified medical vision-language model with Dynamic Cross-modal learnable Prompts. UniDCP is capable of performing all 8 medical uni-modal and cross-modal tasks over 14 corresponding datasets.
• Score: 14.681493967465693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical vision-language pre-training (Med-VLP) models have recently accelerated the fast-growing medical diagnostics application. However, most Med-VLP models learn task-specific representations independently from scratch, thereby leading to great inflexibility when they work across multiple fine-tuning tasks. In this work, we propose UniDCP, a Unified medical vision-language model with Dynamic Cross-modal learnable Prompts, which can be plastically applied to multiple medical vision-language tasks. Specifically, we explicitly construct a unified framework to harmonize diverse inputs from multiple pretraining tasks by leveraging cross-modal prompts for unification, which accordingly can accommodate heterogeneous medical fine-tuning tasks. Furthermore, we conceive a dynamic cross-modal prompt optimizing strategy that optimizes the prompts within the shareable space for implicitly processing the shareable clinic knowledge. UniDCP is the first Med-VLP model capable of performing all 8 medical uni-modal and cross-modal tasks over 14 corresponding datasets, consistently yielding superior results over diverse state-of-the-art methods.

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