HSQ-VLM: 一种用于糖尿病视网膜病变可解释性的新型空间约束象限分割VLM模型
HSQ-VLM: A Novel Spatially-Constrained Quadrant Segmentation VLM Model for Explainability in Diabetic Retinopathy
AI总结：
提出HSQ-VLM，利用地标锚定笛卡尔交叉注意力机制和四象限拓扑潜在分割，实现眼底图像中病变的解剖精确量化与自然语言报告生成，在出血和微动脉瘤检测上达到99.6%和96.4%的灵敏度。
摘要：
糖尿病视网膜病变（DR）是一种侵袭性视网膜疾病，也是全球失明的主要原因，但其临床管理目前受到诊断AI黑箱性质的阻碍。虽然深度学习模型实现了高分类准确率，但严重缺乏能够详细描述导致DR临床决策的确切解剖标志和病变分布的可解释性方法。因此，我们提出了HSQ-VLM，一种新颖的眼底图像象限分割流水线，利用地标锚定笛卡尔交叉注意力机制将视觉特征提取与结构化临床推理统一起来。与依赖任意图像分割的传统方法不同，我们的流水线实现了四象限拓扑潜在分割（TLP），以动态地将视网膜特征与以中央凹为中心的坐标系对齐。这使得视觉语言模型能够生成以解剖精度量化病理的自然语言报告。在包含3,500张高分辨率眼底图像的数据集上，这种创新方法实现了出血检测灵敏度99.6%和微动脉瘤检测灵敏度96.4%，同时与标准分割基线相比，边界模糊误差显著减少。
英文摘要：
Diabetic Retinopathy (DR) is an aggressive retinal disease and a leading cause of global blindness, yet its clinical management is currently hindered by the black-box nature of diagnostic AI. While deep learning models achieve high classification accuracy, there is a critical lack of explainability methods capable of detailing the exact anatomical landmarks and lesion distributions that lead to a clinical decision for DR. Therefore, we propose HSQ-VLM, a novel quadrant segmentation pipeline on fundus images that utilizes a Landmark-Anchored Cartesian Cross-Attention mechanism to unify visual feature extraction with structured clinical reasoning. Unlike traditional methods that rely on arbitrary image partitioning, our pipeline implements 4-quadrant Topological Latent Partitioning (TLP) to dynamically align retinal features with a fovea-centered coordinate system. This allows the Vision-Language Model to generate natural language reports that quantify pathology with anatomical precision. On a dataset of 3,500 high-resolution fundus images, this innovative methodology achieved a lesion detection sensitivity of 99.6% for hemorrhages and 96.4% for microaneurysms, while demonstrating a significant reduction in boundary-ambiguity errors compared to standard segmentation baselines.
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。
