首个三模态青光眼数据集，助力精准分期诊断
GLEAM: A Multimodal Imaging Dataset and HAMM for Glaucoma Classification
- 构建多模态融合模型，聚焦跨模态特征学习
- 在四阶段标注数据上实现高精度分类
- 适合眼科医生与医学影像算法研究者
我们提出青光眼病变评估与分析的多模态成像数据集（GLEAM），这是首个公开可用的三模态青光眼数据集，包含扫描激光眼底成像、视盘周围OCT图像和视野模式偏差图，并标注了四个疾病阶段，可有效利用多模态互补信息，促进各阶段的精准诊断与治疗。为高效融合跨模态信息，我们提出分层注意力掩码建模（HAMM）方法用于多模态青光眼分类。该框架采用分层注意力编码器和轻量解码器，将跨模态表征学习重点集中在编码器上。
原文摘要 · Abstract (English)
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.
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