COROLLA：一种有效的多模态融合框架与监督对比学习青光眼分级
COROLLA: An Efficient Multi-Modality Fusion Framework with Supervised Contrastive Learning for Glaucoma Grading
摘要：
青光眼是一种可导致失明的眼科疾病，早期发现和治疗非常重要。眼底图像和光学相干断层扫描（OCT）图像都是广泛使用的诊断青光眼的方式。然而，现有的青光眼分级方法主要利用单一的模态，忽略了眼底和OCT之间的互补信息。在本文中，我们提出了一个有效的多模态监督对比学习框架，名为COROLLA，青光眼分级。通过层分割以及厚度计算和投影，从原始OCT体积中提取视网膜厚度图并用作替代模态，从而以更少的内存使用实现更有效的计算。鉴于医学图像样本之间的高度结构和分布相似性，我们采用监督对比学习来提高模型的区分能力，并具有更好的收敛性。此外，特征级融合的配对眼底图像和厚度图进行，以提高诊断的准确性。在GAMMA数据集上，与最先进的方法相比，我们的COROLLA框架实现了压倒性的青光眼分级性能。
英文摘要：
Glaucoma is one of the ophthalmic diseases that may cause blindness, for which early detection and treatment are very important. Fundus images and optical coherence tomography (OCT) images are both widely-used modalities in diagnosing glaucoma. However, existing glaucoma grading approaches mainly utilize a single modality, ignoring the complementary information between fundus and OCT. In this paper, we propose an efficient multi-modality supervised contrastive learning framework, named COROLLA, for glaucoma grading. Through layer segmentation as well as thickness calculation and projection, retinal thickness maps are extracted from the original OCT volumes and used as a replacing modality, resulting in more efficient calculations with less memory usage. Given the high structure and distribution similarities across medical image samples, we employ supervised contrastive learning to increase our models' discriminative power with better convergence. Moreover, feature-level fusion of paired fundus image and thickness map is conducted for enhanced diagnosis accuracy. On the GAMMA dataset, our COROLLA framework achieves overwhelming glaucoma grading performance compared to state-of-the-art methods.
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