Uni-Mlip: Unified Self-Supervision for Medical Vision Language Pre-training


Ameera Ali Bawazir (Technology Innovation Institute ), Kebin Wu (Technology Innovation Institute), Wenbin LI (Technology Innovation Institute)
The 35th British Machine Vision Conference
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Abstract

Recent advancements in vision-language pre-training via contrastive learning have significantly improved performance across computer vision tasks. However, in the medical domain, obtaining multimodal data is often costly and challenging due to privacy, sensitivity, and annotation complexity. To mitigate data scarcity while boosting model performance, we introduce Uni-Mlip, a unified self-supervision framework specifically designed to enhance medical vision-language pre-training. Uni-Mlip seamlessly integrates cross-modality, uni-modality, and fused-modality self-supervision techniques at the data-level and the feature-level. Additionally, Uni-Mlip tailors uni-modal image self-supervision to accommodate the unique characteristics of medical images. Our experiments across datasets of varying scales demonstrate that Uni-Mlip significantly surpasses current state-of-the-art methods in three key downstream tasks: image-text retrieval, image classification, and visual question answering (VQA).

Citation

@inproceedings{Bawazir_2024_BMVC,
author    = {Ameera Ali Bawazir and Kebin Wu and Wenbin LI},
title     = {Uni-Mlip: Unified Self-Supervision for Medical Vision Language Pre-training},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0329.pdf}
}


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