轻量级模型通过因果解耦提升青光眼检测可靠性。
LightHCG: a Lightweight yet powerful HSIC Disentanglement based Causal Glaucoma Detection Model framework
- 基于HSIC与图自编码器实现潜空间因果解耦
- 参数量减少93~99%，分类准确率仍超主流模型
- 适合临床干预分析，可解释性强
青光眼作为典型视神经退行性疾病，因不可逆性及对视野的严重损害威胁数百万患者。其主要由眼内压升高或视网膜新生血管导致的视神经损伤引起。传统诊断依赖视野检查、视盘观察和测压仪测量。近年来，基于视网膜图像或OCT的VGG16、Vision Transformers（ViT）等视觉模型在青光眼检测与视杯分割中表现优异。但现有AI方法仍存在可靠性不足、参数过多、潜在虚假关联及难以支持干预分析等问题。为此，本文提出轻量级因果驱动模型LightHCG，采用卷积变分自编码器构建极简潜空间表示，结合HSIC-based隐空间解耦与图自编码器无监督因果表征学习，在保持93~99%参数压缩的同时，显著提升青光眼分类性能，并增强对临床干预分析的支持能力，优于InceptionV3、MobileNetV2、VGG16等先进模型。
原文摘要 · Abstract (English)
As a representative optic degenerative condition, glaucoma has been a threat to millions due to its irreversibility and severe impact on human vision fields. Mainly characterized by dimmed and blurred visions, or peripheral vision loss, glaucoma is well known to occur due to damages in the optic nerve from increased intraocular pressure (IOP) or neovascularization within the retina. Traditionally, most glaucoma related works and clinical diagnosis focused on detecting these damages in the optic nerve by using patient data from perimetry tests, optic papilla inspections and tonometer-based IOP measurements. Recently, with advancements in computer vision AI models, such as VGG16 or Vision Transformers (ViT), AI-automatized glaucoma detection and optic cup segmentation based on retinal fundus images or OCT recently exhibited significant performance in aiding conventional diagnosis with high performance. However, current AI-driven glaucoma detection approaches still have significant room for improvement in terms of reliability, excessive parameter usage, possibility of spurious correlation within detection, and limitations in applications to intervention analysis or clinical simulations. Thus, this research introduced a novel causal representation driven glaucoma detection model: LightHCG, an extremely lightweight Convolutional VAE-based latent glaucoma representation model that can consider the true causality among glaucoma-related physical factors within the optic nerve region. Using HSIC-based latent space disentanglement and Graph Autoencoder based unsupervised causal representation learning, LightHCG not only exhibits higher performance in classifying glaucoma with 93~99% less weights, but also enhances the possibility of AI-driven intervention analysis, compared to existing advanced vision models such as InceptionV3, MobileNetV2 or VGG16.
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