Abstract
The success of large-scale pre-trained vision-language models (VLM) has provided a promising direction of transferring natural image representations to the medical domain by providing a well-designed prompt with medical expert-level knowledge. However, one prompt has difficulty in describing the medical lesions thoroughly enough and containing all the attributes. Besides, the models pre-trained with natural images fail to detect lesions precisely. To solve this problem, fusing multiple prompts is vital to assist the VLM in learning a more comprehensive alignment between textual and visual modalities. In this paper, we propose an ensemble guided fusion approach to leverage multiple statements when tackling the phrase grounding task for zero-shot lesion detection. Extensive experiments are conducted on three public medical image datasets across different modalities and the detection accuracy improvement demonstrates the superiority of our method.
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Guo, M., Yi, H., Qin, Z., Wang, H., Men, A., Lao, Q. (2023). Multiple Prompt Fusion for Zero-Shot Lesion Detection Using Vision-Language Models. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14224. Springer, Cham. https://doi.org/10.1007/978-3-031-43904-9_28
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