MMCLIP: Cross-Modal Attention Masked Modelling for Medical Language-Image Pre-Training

Vision-and-language pretraining (VLP) in the medical field utilizes contrastive learning on image-text pairs to achieve effective transfer across tasks. To further enhance visual and textual representation learning, many recent approaches adopt masked modeling strategies that randomly hide input tokens during training. Nevertheless, the adoption of masked modeling strategies in existing VLP methods gives rise to two key challenges in medical applications. First, current models struggle to accurately reconstruct key pathological features due to the scarcity of medical data. Second, most methods only adopt either paired image-text or image-only data, failing to exploit the combination of both paired and unpaired data. To this end, this paper proposes the MMCLIP (Masked Medical Contrastive Language-Image Pre-Training) framework to enhance pathological learning and feature learning via unpaired data. First, we introduce the attention-masked image modelling (AttMIM) and entity-driven masked language modelling module (EntMLM), which learns to reconstruct pathological visual and textual tokens via multi-modal feature interaction, thus improving medical-enhanced features. The AttMIM module masks a portion of the image features that are highly responsive to textual features. This allows MMCLIP to improve the reconstruction of highly similar image data in medicine efficiency. The EntMLM module identifies and masks key medical entities in the text with Named Entity Recognition (NER), and reconstructs them with support from visual features, enabling richer understanding of disease-related language. Second, our MMCLIP capitalizes unpaired data to enhance multimodal learning by introducing disease-kind prompts. The experimental results show that MMCLIP achieves SOTA for zero-shot and fine-tuning classification performance on five datasets. Our code will be available at https://github.com/AIGeeksGroup/MMCLIP . CCS Concepts • Computing methodologies → Image representations.