发表机构
Fondazione Human Technopole; Università Campus Bio-Medico（人类技术基金会; 校园生物医学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对大图像数据平铺预测产生的伪影问题，提出SWITi方法，通过平均重叠滑动窗口预测减少伪影，引入FRT和ASV指标量化检测伪影，在荧光显微镜数据集模型上验证该方法可减弱拼接缝、提升重建效果，利于生物医学大图像预测下游处理。
AI 中文摘要
SWITi是一种在测试时减少平铺预测中伪影的方法，尤其适用于在推理时从学习的后验分布中采样解决方案的神经网络。对于大图像数据，平铺预测不可避免，当平铺小于网络感受野且平铺为独立后验样本时会产生伪影。SWITi对重叠滑动窗口预测求平均，使相邻样本差异分散。对于后验模型，其使用的平铺样本不比MMSE估计所需的多，无需额外前向传播。还引入两个无参考指标FRT和ASV检测量化伪影。在三个荧光显微镜数据集的预训练和已发布图像分割模型上，SWITi显著减弱拼接缝，提高重建保真度和分辨率，对生物医学数据的大图像预测下游处理有重要意义。
英文摘要
SWITi is a test-time method for reducing artifacts in tiled predictions, particularly for neural networks that learn posterior distributions from which solutions are sampled at inference time. Tiled predictions are unavoidable for large image data, and artifacts arise whenever tiles are smaller than a network's receptive field and when tiles are independent posterior samples. SWITi averages overlapping sliding-window predictions, so discrepancies between neighboring samples are spread across shifted tile positions rather than accumulating at fixed seam coordinates. For posterior models, SWITi uses no more tile samples than an MMSE estimate requires and therefore incurs no additional forward passes. Additionally, we introduce two reference-free metrics, the Fraction of Rejected Tests (FRT) and Artifact Severity (ASV), for detecting and quantifying tiling artifacts from a per-tile permutation test that compares the distribution of pixel gradients across tile seams against the surrounding image content. On pre-trained and published image splitting models across three fluorescence microscopy datasets in 2D and 3D, we show that SWITi substantially attenuates stitching seams while also improving reconstruction fidelity and resolution. Since tiling artifacts in posterior predictions can easily be mistaken for biological structures or for boundaries between biological structures, removing or reducing them using SWITi will improve the downstream processing of large image predictions, which is particularly relevant for biomedical data.
