发表机构
Uppsala University; Technical University of Darmstadt（乌普萨拉大学; 达姆施塔特工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
计算细胞学中恶性细胞检测难，现有方法有局限。本文提出D4等变扩散框架，通过结构和推理时的对称强制，实现变换一致的重建与稳定异常排名，在两个细胞学数据集上表现优于其他方法，降低分数方差。
AI 中文摘要
全玻片图像的计算细胞学具有挑战性，因为恶性细胞罕见、异质性强且带注释的玻片稀缺。异常检测框架可在正常玻片阴性补丁上训练，然后在测试时用于标记未见过的玻片上的异常补丁。大多数无监督异常检测方法，包括生成式方法（基于GAN和基于扩散的），是针对器官级成像调整的，需要大量精心策划的数据集。在细胞学中，信号是以细胞为中心的：旋转或翻转单个细胞补丁不会改变其诊断类别，但标准扩散模型将变换后的视图视为不同的输入，导致依赖变换的重建和不稳定的异常分数。我们提出了一个D4等变扩散框架，该框架通过一个D4等变U-Net在结构上以及通过等变噪声耦合和（可选）帧平均在推理时强制旋转和反射对称。这种与生物不变性的对齐产生了变换一致的伪健康重建，并在对称下实现了更稳定的异常排名。在两个公开可用的骨髓和外周血涂片细胞学数据集上，我们的D4等变扩散模型比非等变生成基线、深度单类和基于多实例学习的方法实现了更高的AUC，并且在顶部K预测中检索到更多异常细胞，同时大幅降低了旋转和翻转时的分数方差。代码可在此https URL获取。
英文摘要
Computational cytology on whole-slide images is challenging because malignant cells are rare, heterogeneous, and annotated slides are scarce. Anomaly detection frameworks can be trained on normal slide-negative patches and then applied at test time to flag abnormal patches in held-out slides. Most unsupervised anomaly detection approaches including generative ones (GAN-based and diffusion-based), are tuned to organ-level imaging and require large curated datasets. In cytology the signal is cell-centric: rotating or flipping a single-cell patch does not change its diagnostic class, yet standard diffusion models treat transformed views as distinct inputs, leading to transformation-dependent reconstructions and unstable anomaly scores. We propose a D4-equivariant diffusion framework that enforces rotation and reflection symmetry both architecturally, via a D4-equivariant U-Net, and at inference, via equivariant noise coupling and (optionally) frame averaging. This alignment with biological invariance yields transformation-consistent pseudo-healthy reconstructions and more stable anomaly ranking under symmetry. On two publicly available cytology datasets of bone marrow and peripheral blood smears, our D4-equivariant diffusion models achieve higher AUC and retrieve more abnormal cells in the top K predictions than non-equivariant generative baselines, a deep one-class, and a multiple instance learning based method, while substantially reducing score variance across rotations and flips. Code is available at https://swchmida.github.io/D4diffCyto/.
Comments11 pages, 2 figures, 1 table, 1 algorithm. Accepted for publication in MICCAI 2026
