MM-Retinal: Knowledge-Enhanced Foundational Pretraining with Fundus Image-Text Expertise
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909752450088960 |
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| author | Wu, Ruiqi Zhang, Chenran Zhang, Jianle Zhou, Yi Zhou, Tao Fu, Huazhu |
| author_facet | Wu, Ruiqi Zhang, Chenran Zhang, Jianle Zhou, Yi Zhou, Tao Fu, Huazhu |
| contents | Current fundus image analysis models are predominantly built for specific tasks relying on individual datasets. The learning process is usually based on data-driven paradigm without prior knowledge, resulting in poor transferability and generalizability. To address this issue, we propose MM-Retinal, a multi-modal dataset that encompasses high-quality image-text pairs collected from professional fundus diagram books. Moreover, enabled by MM-Retinal, we present a novel Knowledge-enhanced foundational pretraining model which incorporates Fundus Image-Text expertise, called KeepFIT. It is designed with image similarity-guided text revision and mixed training strategy to infuse expert knowledge. Our proposed fundus foundation model achieves state-of-the-art performance across six unseen downstream tasks and holds excellent generalization ability in zero-shot and few-shot scenarios. MM-Retinal and KeepFIT are available at https://github.com/lxirich/MM-Retinal. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11793 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MM-Retinal: Knowledge-Enhanced Foundational Pretraining with Fundus Image-Text Expertise Wu, Ruiqi Zhang, Chenran Zhang, Jianle Zhou, Yi Zhou, Tao Fu, Huazhu Computer Vision and Pattern Recognition Current fundus image analysis models are predominantly built for specific tasks relying on individual datasets. The learning process is usually based on data-driven paradigm without prior knowledge, resulting in poor transferability and generalizability. To address this issue, we propose MM-Retinal, a multi-modal dataset that encompasses high-quality image-text pairs collected from professional fundus diagram books. Moreover, enabled by MM-Retinal, we present a novel Knowledge-enhanced foundational pretraining model which incorporates Fundus Image-Text expertise, called KeepFIT. It is designed with image similarity-guided text revision and mixed training strategy to infuse expert knowledge. Our proposed fundus foundation model achieves state-of-the-art performance across six unseen downstream tasks and holds excellent generalization ability in zero-shot and few-shot scenarios. MM-Retinal and KeepFIT are available at https://github.com/lxirich/MM-Retinal. |
| title | MM-Retinal: Knowledge-Enhanced Foundational Pretraining with Fundus Image-Text Expertise |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.11793 |