用相似网络与排序距离，自动预测眼底OCT中黄斑病变活动变化。
Automatic detection and prediction of nAMD activity change in retinal OCT using Siamese networks and Wasserstein Distance for ordinality
- 用视觉变换器构建双胞胎网络，对比不同时间点的眼底扫描特征
- 首次引入沃尔德斯坦距离损失，捕捉病情恶化等级的有序关系
- 适合眼科医生和医学影像算法研究者，助力精准治疗决策
老年性黄斑变性（nAMD）是老年人视力丧失的主要原因，疾病活动检测与进展预测对及时用药和改善患者预后至关重要。深度学习为从光学相干断层扫描（OCT）视网膜体积中预测AMD变化提供了新路径。本文针对MICCAI 2024 MARIO挑战赛提出两个任务模型：第一，采用基于视觉变换器（ViT）的双胞胎网络，通过比较患者不同时期扫描嵌入向量来检测病情严重程度变化；第二，首次利用沃尔德斯坦（Wasserstein）距离损失函数建模严重程度变化类别的有序性，以预测3个月后的病情演变。两个模型在初步排行榜中表现优异，证明其预测能力有助于提升nAMD治疗管理效率。
原文摘要 · Abstract (English)
Neovascular age-related macular degeneration (nAMD) is a leading cause of vision loss among older adults, where disease activity detection and progression prediction are critical for nAMD management in terms of timely drug administration and improving patient outcomes. Recent advancements in deep learning offer a promising solution for predicting changes in AMD from optical coherence tomography (OCT) retinal volumes. In this work, we proposed deep learning models for the two tasks of the public MARIO Challenge at MICCAI 2024, designed to detect and forecast changes in nAMD severity with longitudinal retinal OCT. For the first task, we employ a Vision Transformer (ViT) based Siamese Network to detect changes in AMD severity by comparing scan embeddings of a patient from different time points. To train a model to forecast the change after 3 months, we exploit, for the first time, an Earth Mover (Wasserstein) Distance-based loss to harness the ordinal relation within the severity change classes. Both models ranked high on the preliminary leaderboard, demonstrating that their predictive capabilities could facilitate nAMD treatment management.
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