Medical Cross-Modal Prompt Hashing with Robust Noisy Correspondence Learning

In the realm of medical data analysis, medical cross-modal hashing (Med-CMH) has emerged as a promising approach to facilitate fast similarity search across multi-modal medical data. However, due to human subjective deviation or semantic ambiguity, the presence of noisy correspondence across medical modalities exacerbates the challenge of the heterogeneous gap in cross-modal learning. To eliminate clinical noisy correspondence, this paper proposes a novel medical cross-modal prompt hashing (MCPH) that incorporates multi-modal prompt optimization with noise-robust contrastive constraint for facilitating noisy correspondence issues. Benefitting from the robust reasoning capabilities inherent in medical large-scale models, we design a visual-textual prompt learning paradigm to collaboratively enhance alignment and contextual awareness between the medical visual and textual representations. By providing targeted prompts and cues from the medical large language model (LLM), i.e., CheXagent, multi-modal prompt learning facilitates the extraction of relevant features and associations, empowering the model with actionable insights and decision support. Furthermore, a noise-robust contrastive learning strategy is dedicated to dynamically adjusting the intensity of contrastive learning across modalities, thereby enhancing the contrast strength of positive pairs while mitigating the influence of noisy correspondence pairs. Extensive experiments on multiple benchmark datasets demonstrate that our MCPH surpasses the state-of-the-art baselines.