Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training (2209.07098v1)

Abstract: Medical vision-and-language pre-training provides a feasible solution to extract effective vision-and-language representations from medical images and texts. However, few studies have been dedicated to this field to facilitate medical vision-and-language understanding. In this paper, we propose a self-supervised learning paradigm with multi-modal masked autoencoders (M$^3$AE), which learn cross-modal domain knowledge by reconstructing missing pixels and tokens from randomly masked images and texts. There are three key designs to make this simple approach work. First, considering the different information densities of vision and language, we adopt different masking ratios for the input image and text, where a considerably larger masking ratio is used for images. Second, we use visual and textual features from different layers to perform the reconstruction to deal with different levels of abstraction in visual and language. Third, we develop different designs for vision and language decoders (i.e., a Transformer for vision and a multi-layer perceptron for language). To perform a comprehensive evaluation and facilitate further research, we construct a medical vision-and-language benchmark including three tasks. Experimental results demonstrate the effectiveness of our approach, where state-of-the-art results are achieved on all downstream tasks. Besides, we conduct further analysis to better verify the effectiveness of different components of our approach and various settings of pre-training. The source code is available at~\url{https://github.com/zhjohnchan/M3AE}.

Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training

The paper "Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training" presents a novel approach to enhance understanding of medical images and associated textual data via vision-and-language pre-training using a self-supervised learning paradigm. The discussed method focuses on extracting cross-modal representations from medical image-text pairs through the deployment of Multi-Modal Masked Autoencoders (M$^3$AE). This approach addresses the intricate challenge of obtaining effective representations in the medical domain, which is complicated by the lack of large-scale labeled datasets and the necessity of incorporating domain-specific knowledge.

Key Contributions

The proposed M$^3$AE methodology is characterized by several significant contributions:

1. Multi-Modal Autoencoder Design: The design utilizes autoencoders to mask and reconstruct missing pixels and tokens within both images and text. The usage of differing masking ratios between visual and textual inputs is a cornerstone of this method, reflecting the disparate information densities inherent in these modalities. A more aggressive masking strategy is employed for image data compared to textual data.
2. Reconstruction Layer Strategy: For reconstruction, the model employs visual and textual features from different layers of abstraction. Visual features are taken from intermediate layers, whereas textual features are derived from final outputs, acknowledging the varied semantic levels present in image and language data.
3. Differential Decoder Designs: The paper introduces distinct decoder architectures for processing the visual and textual information. Specifically, the visual pathway utilizes a Transformer-based model, while textual processing is handled through a Multi-Layer Perceptron (MLP).

To evaluate the proposed approach, the paper introduces a comprehensive benchmark for medical vision-and-language understanding, comprising tasks such as Medical Visual Question Answering (Med-VQA), medical image-text classification, and retrieval. Experimental results underscore M$^3$AE's success by achieving state-of-the-art performance across all cited tasks.

Empirical Evaluation

In the empirical evaluations, the paper reports that their method surpasses previous standards on datasets including VQA-RAD, SLAKE, and VQA-2019. The results demonstrate the effectiveness of implementing the M$^3$AE with multi-modal data under the pre-train-and-fine-tune paradigm. Notably, performance enhancements observed in tasks like Med-VQA emphasize the significance of robust cross-modal representations for resolving complex medical questions based on visual data.

Practical and Theoretical Implications

The practical implications of this research are vast in the medical informatics sector, as improvements in automated understanding of medical imagery and associated reports can enhance clinical decision-making processes. Theoretically, the work also contributes to the development of multi-modal representation learning frameworks, particularly in domains characterized by substantial domain-specific knowledge and inherently linked multi-modal data, such as healthcare.

Future Directions

Future advancements in this area could explore the integration of domain-specific knowledge enhancing the self-supervised paradigm, potentially improving contextual understanding of more nuanced medical data. Another prospective direction could involve the development of domain-agnostic models that can effectively generalize across various topics beyond the medical domain using similar multi-modal strategies.

Overall, this paper adds significant value to the field of medical AI by proposing an innovative approach to improve vision-and-language integration using masked autoencoders, thereby enhancing the potential for automated medical data interpretation.