通过领域泛化和稳定的稀疏嵌入签名实现放大不变的图像分类
Magnification-Invariant Image Classification via Domain Generalization and Stable Sparse Embedding Signatures
AI总结：
本文提出通过领域泛化和稳定稀疏嵌入签名解决放大偏移问题，验证了领域泛化模型在不同放大倍数下的优越性能，且在保持预测性能的同时显著减少了嵌入维度。
摘要：
放大偏移是稳健病理科分类的主要障碍，因为训练在一种成像尺度上的模型往往难以推广到另一种尺度。本文在BreaKHis数据集上评估了这一问题，采用严格的患者不重叠留一放大倍数出协议，比较了监督基线、基线增强DCGAN生成的补丁以及设计用于保留判别信息并抑制放大特定变化的梯度反转域泛化模型。在留出的放大倍数上，域泛化模型实现了最强的整体判别能力，其最显著的收益出现在200X被留出时。相比之下，GAN增强产生了不一致的效果，改善了一些折叠但损害了其他，特别是在400X时。域泛化模型还产生了最低的Brier分数，0.063 vs 0.089在基线。稀疏嵌入分析进一步揭示，域泛化训练将平均签名大小减少了超过三倍（306 vs 1,074维），同时保持了等效的预测性能（AUC: 0.967 vs 0.965; F1: 0.930 vs 0.931）。它还增加了跨折叠签名的可重复性，从基线的近零Jaccard重叠增加到100X和200X折叠之间0.99。这些发现表明，经过校准、紧凑且可转移的表示可以在不增加架构复杂性的情况下学习，对计算病理学模型在异质获取设置中的可靠部署有明确的启示。
英文摘要：
Magnification shift is a major obstacle to robust histopathology classification, because models trained on one imaging scale often generalize poorly to another. Here, we evaluated this problem on the BreaKHis dataset using a strict patient-disjoint leave-one-magnification-out protocol, comparing supervised baseline, baseline augmented with DCGAN-generated patches, and a gradient-reversal domain-general model designed to preserve discriminative information while suppressing magnification-specific variation. Across held-out magnifications, the domain-general model achieved the strongest overall discrimination and its clearest gain was observed when 200X was held out. By contrast, GAN augmentation produced inconsistent effects, improving some folds but degrading others, particularly at 400X. The domain-general model also yielded the lowest Brier score at 0.063 vs 0.089 at baseline. Sparse embedding analysis further revealed that domain-general training reduced average signature size more than three-fold (306 versus 1,074 dimensions) while preserving equivalent predictive performance (AUC: 0.967 vs 0.965; F1: 0.930 vs 0.931). It also increased cross-fold signature reproducibility from near-zero Jaccard overlap in the baseline to 0.99 between the 100X and 200X folds. These findings show that calibrated, compact, and transferable representations can be learned without added architectural complexity, with clear implications for the reliable deployment of computational pathology models across heterogeneous acquisition settings.
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。
