Masked Contrastive Reconstruction for Cross-modal Medical Image-Report Retrieval

TL;DR

This paper introduces MCR, a novel VLP framework that uses masked data for both contrastive learning and reconstruction, improving cross-modal medical image-report retrieval efficiency and accuracy.

Contribution

The paper proposes MCR, a unified masked data approach for contrastive and reconstruction tasks, reducing interference and memory use, with a new modality alignment strategy MbA for better semantic matching.

Findings

Achieves state-of-the-art results on MIMIC-CXR dataset.

Reduces GPU memory and training time significantly.

Enhances semantic consistency in cross-modal retrieval.

Abstract

Cross-modal medical image-report retrieval task plays a significant role in clinical diagnosis and various medical generative tasks. Eliminating heterogeneity between different modalities to enhance semantic consistency is the key challenge of this task. The current Vision-Language Pretraining (VLP) models, with cross-modal contrastive learning and masked reconstruction as joint training tasks, can effectively enhance the performance of cross-modal retrieval. This framework typically employs dual-stream inputs, using unmasked data for cross-modal contrastive learning and masked data for reconstruction. However, due to task competition and information interference caused by significant differences between the inputs of the two proxy tasks, the effectiveness of representation learning for intra-modal and cross-modal features is limited. In this paper, we propose an efficient VLP framework named Masked Contrastive and Reconstruction (MCR), which takes masked data as the sole input for both tasks. This enhances task connections, reducing information interference and competition between them, while also substantially decreasing the required GPU memory and training time. Moreover, we introduce a new modality alignment strategy named Mapping before Aggregation (MbA). Unlike previous methods, MbA maps different modalities to a common feature space before conducting local feature aggregation, thereby reducing the loss of fine-grained semantic information necessary for improved modality alignment. Qualitative and quantitative experiments conducted on the MIMIC-CXR dataset validate the effectiveness of our approach, demonstrating state-of-the-art performance in medical cross-modal retrieval tasks.