用融合卷积网络与掩码自编码器，精准判断眼底OCT图像的病变进展。
Patch Progression Masked Autoencoder with Fusion CNN Network for Classifying Evolution Between Two Pairs of 2D OCT Slices
- 融合CNN与模型集成提升图像演变分类性能。
- 提出掩码自编码器生成未来OCT图像，实现3个月进展预测。
- 在权威挑战中排名前10，适合眼科AI与医学影像研究者参考。
年龄相关性黄斑变性（AMD）是一种常见的眼部疾病，影响视力。抗血管内皮生长因子（anti-VEGF）治疗对新生血管型AMD有效，及时诊断与持续监测可改善预后。通过追踪渗出性AMD患者OCT扫描中的新生血管进展，有助于制定更个性化的治疗方案。本研究参与了光学相干断层扫描中年龄相关性黄斑变性进展监测（MARIO）挑战赛。在任务1中，针对连续两次OCT采集的两组2D切片的演变分类，采用融合卷积神经网络并结合模型集成以提升性能。在任务2中，基于当前检查数据预测未来三个月的进展，提出一种分块进展掩码自编码器，先生成下一次检查的OCT图像，再利用任务1的解决方案对当前与生成的OCT图像进行演变分类。最终结果使我们在两项任务中均位列前十。部分团队成员与挑战赛组织方同属一机构，因此不具获奖资格。
原文摘要 · Abstract (English)
Age-related Macular Degeneration (AMD) is a prevalent eye condition affecting visual acuity. Anti-vascular endothelial growth factor (anti-VEGF) treatments have been effective in slowing the progression of neovascular AMD, with better outcomes achieved through timely diagnosis and consistent monitoring. Tracking the progression of neovascular activity in OCT scans of patients with exudative AMD allows for the development of more personalized and effective treatment plans. This was the focus of the Monitoring Age-related Macular Degeneration Progression in Optical Coherence Tomography (MARIO) challenge, in which we participated. In Task 1, which involved classifying the evolution between two pairs of 2D slices from consecutive OCT acquisitions, we employed a fusion CNN network with model ensembling to further enhance the model's performance. For Task 2, which focused on predicting progression over the next three months based on current exam data, we proposed the Patch Progression Masked Autoencoder that generates an OCT for the next exam and then classifies the evolution between the current OCT and the one generated using our solution from Task 1. The results we achieved allowed us to place in the Top 10 for both tasks. Some team members are part of the same organization as the challenge organizers; therefore, we are not eligible to compete for the prize.
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