LightHCG: 一种轻量且强大的基于HSIC解耦的因果青光眼检测模型框架
LightHCG: a Lightweight yet powerful HSIC Disentanglement based Causal Glaucoma Detection Model framework
AI总结：
LightHCG是一种轻量且强大的基于HSIC解耦的因果青光眼检测模型，通过减少参数和提升因果分析能力，改进了青光眼检测的性能和应用范围。
摘要：
作为一种典型的视神经退行性病变，青光眼由于其不可逆性和对人类视野的严重影响，已成为威胁数百万人的疾病。主要特征是视野变暗、模糊或视野缺损，青光眼通常由于视神经损伤导致眼内压（IOP）升高或视网膜内新血管形成而发生。传统上，大多数与青光眼相关的研究和临床诊断主要通过患者进行的视野检查、视神经盘检查和基于压差计的IOP测量数据来检测这些损伤。最近，随着计算机视觉AI模型的进步，如VGG16或视觉Transformer（ViT），基于视网膜底片图像或OCT的AI自动青光眼检测和视神经杯分割在辅助传统诊断方面表现出显著的性能。然而，当前AI驱动的青光眼检测方法在可靠性、参数使用过多、检测中可能存在虚假相关性以及在干预分析或临床模拟中的应用限制方面仍有显著改进空间。因此，本研究提出了一种新的因果表示驱动的青光眼检测模型：LightHCG，一种极轻量的基于卷积VAE的潜在青光眼表示模型，可以考虑视神经区域中与青光眼相关的物理因素之间的真正因果关系。使用基于HSIC的潜在空间解耦和基于图自动编码器的无监督因果表示学习，LightHCG不仅在分类青光眼方面表现出更高的性能，其权重减少了93~99%，并且与现有先进的视觉模型如InceptionV3、MobileNetV2或VGG16相比，增强了AI驱动的干预分析的可能性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
