用几何拓扑感知方法提升叶片病斑分割精度，尤其改善边界和孔洞结构。
HyperTopo-Adapters: Geometry- and Topology-Aware Segmentation of Leaf Lesions on Frozen Encoders
- 在双曲+欧氏+球面流形上嵌入特征，兼顾层级分离、局部细节与全局闭合。
- 拓扑先验使病斑孔洞误差降低9%，边界F1和贝蒂数误差显著改善。
- 适合关注生物形态结构的植物病害分析，开源代码可复现验证。
叶片病斑分割对拓扑结构敏感：微小的合并、分裂或虚假孔洞可能反映生化通路，但标准像素损失在欧氏潜空间中对其惩罚不足。本文提出轻量级、参数高效的HyperTopo-Adapters，基于冻结视觉编码器，在超曲面流形（双曲+欧氏+球面，H+E+S）上嵌入特征，以促进层级分离（H）、局部线性细节（E）和全局闭合（S）。引入拓扑先验：(i) 通过持久同调（PH）距离评估与选择；(ii) 可微分代理函数结合软欧拉特征匹配与总变差正则化实现稳定训练。设计了双曲对比项与拓扑先验的预热策略，支持样本级结构度量（边界-F1、贝蒂误差、帕丁顿距离），并采用最小帕丁顿距离筛选前K个最高Dice分数模型。在Kaggle叶斑数据集（N=2,940）上，早期结果表明边界与拓扑指标持续提升（Δβ₁孔洞误差减少9%），同时保持竞争力的Dice/IoU。研究为诊断性设计，报告了控制性消融实验（曲率学习、潜空间维度、对比温度、代理设置），并持续测试编码器强度（ResNet-50、DeepLabV3、DINOv2/v3）、输入分辨率、PH权重及部分解冻深层模块。贡献为开放可复现的训练/评估套件（见https://github.com/ChimdiWalter/HyperTopo-Adapters），隔离几何/拓扑先验并揭示失效模式，指导更优的拓扑保真架构设计。
原文摘要 · Abstract (English)
Leaf-lesion segmentation is topology-sensitive: small merges, splits, or false holes can be biologically meaningful descriptors of biochemical pathways, yet they are weakly penalized by standard pixel-wise losses in Euclidean latents. I explore HyperTopo-Adapters, a lightweight, parameter-efficient head trained on top of a frozen vision encoder, which embeds features on a product manifold -- hyperbolic + Euclidean + spherical (H + E + S) -- to encourage hierarchical separation (H), local linear detail (E), and global closure (S). A topology prior complements Dice/BCE in two forms: (i) persistent-homology (PH) distance for evaluation and selection, and (ii) a differentiable surrogate that combines a soft Euler-characteristic match with total variation regularization for stable training. I introduce warm-ups for both the hyperbolic contrastive term and the topology prior, per-sample evaluation of structure-aware metrics (Boundary-F1, Betti errors, PD distance), and a min-PD within top-K Dice rule for checkpoint selection. On a Kaggle leaf-lesion dataset (N=2,940), early results show consistent gains in boundary and topology metrics (reducing Delta beta_1 hole error by 9%) while Dice/IoU remain competitive. The study is diagnostic by design: I report controlled ablations (curvature learning, latent dimensions, contrastive temperature, surrogate settings), and ongoing tests varying encoder strength (ResNet-50, DeepLabV3, DINOv2/v3), input resolution, PH weight, and partial unfreezing of late blocks. The contribution is an open, reproducible train/eval suite (available at https://github.com/ChimdiWalter/HyperTopo-Adapters) that isolates geometric/topological priors and surfaces failure modes to guide stronger, topology-preserving architectures.
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