Improving CLIP for Biomedical Retrieval: A Multi-Margin Contrastive Loss Approach

Biomedical documents contain a wealth of multimodal data, including visual evidence and natural language descriptions. Effectively navigating this vast and expanding data source poses significant challenges, requiring labor-intensive manual curation processes at scale. Multimodal learning methodologies offer a promising approach to mitigate these bottle-necks in domain-specific information retrieval tasks, especially in biomedical sciences. This research investigates the potential of multimodal learning methodologies for various downstream tasks, such as information retrieval and classification based on paired multimodal image-text datasets. The proposed solution employs state-of-the-art transformer-based deep learning techniques and a contrastive learning approach to design a foundational model capable of adapting to biomedical vision-language processing. Through experiments and comparative studies, the effectiveness of our model was validated, demonstrating superior performance compared to existing approaches across diverse standard datasets. This study emphasizes the challenges in domain-specific environments and underscores the critical role of high-quality multimodal data in advancing biomedical AI applications, fostering innovation and progress in healthcare research and practice.