arXiv：cs.LG· Won June Cho, Daeky Jeong, Hyeongyeol Lim, Hongjun Yoon·· 16 小时前
STREAM：面向数字病理图像生成的随机黎曼流匹配与非各向同性解码器
STREAM: Stochastic Riemannian Flow Matching with Anisotropic Decoder for Digital Histopathology Image Generation
AI 导读
STREAM 是首个将黎曼流匹配应用于病理学领域的框架，通过桥式随机扰动在单位超球面 S^{d-1} 上建立逐 token 可矫正性以训练 Diffusion Transformer，并采用噪声协方差在切空间投影速度场 Jacobian 左奇异基上呈非各向同性的解码器。
正文
Abstract:Synthetic histopathology image generation addresses patient-privacy concerns and the growing data demands of foundation models. Existing state-of-the-art histopathology generative models use pretrained Vision Foundation Models (VFMs) as conditioning signals. We show this yields conditioning-dominated diversity: on TCGA-BRCA, 62-75% of their output diversity is attributable to the conditioning signal rather than the learned latent space, while de novo synthesis still requires a VFM at inference. We instead use histopathology VFMs as the latent space itself: their patch tokens are $\ell_2$-normalized on the unit hypersphere $\mathcal{S}^{d-1}$ with strong angular dominance and intrinsic curvature, motivating a Riemannian formulation. We present STREAM, the first framework to apply Riemannian flow matching in the histopathology domain, in two stages: 1) a bridge-type stochastic perturbation that establishes per-token rectifiability on $\mathcal{S}^{d-1}$ for training a Diffusion Transformer, and 2) a novel decoder training design whose noise covariance is anisotropic in the left-singular basis of the per-token tangent-projected velocity-field Jacobian, spending a large robustness budget on its low-response directions and a small one on its high-response directions. Across TCGA-BRCA and TCGA-COADREAD, STREAM achieves state-of-the-art gFID and ranks first on nearly all histopathology-specific metrics as well. Code and a public gallery of generated images are available at this https URL.
| Comments: | Accepted at NeurIPS 2026 as Spotlight |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.07036 [cs.CV] |
| (or arXiv:2606.07036v3 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2606.07036 arXiv-issued DOI via DataCite |
Submission history
From: Won June Cho [view email]
[v1]
Fri, 5 Jun 2026 08:26:44 UTC (8,031 KB)
[v2]
Mon, 5 Oct 2026 12:17:47 UTC (18,543 KB)
[v3]
Wed, 14 Oct 2026 05:30:57 UTC (18,543 KB)
来源：arXiv：cs.LG · arxiv.org