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Multimodal Medical Image Binding via Shared Text Embeddings

Created by
  • Haebom

Author

Yunhao Liu, Suyang Xi, Shiqi Liu, Hong Ding, Chicheng Jin, Chong Zhong, Junjun He, Catherine C. Liu, Yiqing Shen

Outline

This paper presents a method for integrating features from multiple image modalities for diverse medical image analysis. Existing CLIP-based approaches require paired data across different modalities, which is difficult to obtain in medical image data. To address this, we propose a novel pre-training framework, Multimodal Medical Image Binding with Text (M³Bind). M³Bind seamlessly aligns multiple modalities through a shared text representation space without requiring explicit paired data between different medical image modalities. Specifically, M³Bind fine-tunes a pre-trained CLIP-like image-text model to align the text embedding spaces of each modality and then distills modality-specific text encoders into a unified model to generate a shared text embedding space. Experimental results on X-ray, CT, retina, ECG, and pathology images demonstrate that M³Bind outperforms CLIP-like models on zero-shot and few-shot classification and cross-modal retrieval tasks.

Takeaways, Limitations

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Takeaways:
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We present a novel framework that effectively performs modality alignment without requiring explicit paired data between medical image modalities.
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Demonstrated superior performance over existing CLIP-based models in zero-shot and few-shot learning.
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Performance validation in various medical imaging modalities (X-ray, CT, retina, ECG, pathology images).
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Suggesting effective applicability in various downstream tasks (classification, cross-modal search).
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Limitations:
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The performance of M³Bind presented in this paper is based on experimental results on a specific dataset, and generalization performance on other datasets or clinical environments requires additional validation.
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Because the CLIP-like model is used as a pre-trained model, there are aspects that depend on the performance of the CLIP model.
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There is a possibility that data imbalance issues across different modalities and biases toward specific modalities may affect performance.
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Additional research and validation are needed for practical clinical application.
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