GLEAM: A Multimodal Imaging Dataset and HAMM for Glaucoma Classification
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913109125365760 |
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| author | Wang, Jiao Liu, Chi Zhang, Yiying Luo, Hongchen Guo, Zhifen Hu, Ying Xu, Ke Zhou, Jing Xu, Hongyan Zhou, Ruiting Tang, Man |
| author_facet | Wang, Jiao Liu, Chi Zhang, Yiying Luo, Hongchen Guo, Zhifen Hu, Ying Xu, Ke Zhou, Jing Xu, Hongyan Zhou, Ruiting Tang, Man |
| contents | We propose glaucoma lesion evaluation and analysis with multimodal imaging (GLEAM), the first publicly available tri-modal glaucoma dataset comprising scanning laser ophthalmoscopy fundus images, circumpapillary OCT images, and visual field pattern deviation maps, annotated with four disease stages, enabling effective exploitation of multimodal complementary information and facilitating accurate diagnosis and treatment across disease stages. To effectively integrate cross-modal information, we propose hierarchical attentive masked modeling (HAMM) for multimodal glaucoma classification. Our framework employs hierarchical attentive encoders and light decoders to focus cross-modal representation learning on the encoder. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12800 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GLEAM: A Multimodal Imaging Dataset and HAMM for Glaucoma Classification Wang, Jiao Liu, Chi Zhang, Yiying Luo, Hongchen Guo, Zhifen Hu, Ying Xu, Ke Zhou, Jing Xu, Hongyan Zhou, Ruiting Tang, Man Image and Video Processing Computer Vision and Pattern Recognition We propose glaucoma lesion evaluation and analysis with multimodal imaging (GLEAM), the first publicly available tri-modal glaucoma dataset comprising scanning laser ophthalmoscopy fundus images, circumpapillary OCT images, and visual field pattern deviation maps, annotated with four disease stages, enabling effective exploitation of multimodal complementary information and facilitating accurate diagnosis and treatment across disease stages. To effectively integrate cross-modal information, we propose hierarchical attentive masked modeling (HAMM) for multimodal glaucoma classification. Our framework employs hierarchical attentive encoders and light decoders to focus cross-modal representation learning on the encoder. |
| title | GLEAM: A Multimodal Imaging Dataset and HAMM for Glaucoma Classification |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.12800 |