发表机构
The University of Tokyo Hospital; Kanazawa University Hospital（东京大学医学部附属医院; 金泽大学附属医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出准二值化信息瓶颈层，构建QBAE自编码器，在医学图像异常检测中实现架构无关的互信息约束，并在MedIAnomaly基准上取得领先性能。
AI 中文摘要
无监督异常检测仅从正常图像中学习，是医学图像分析中的核心任务，且仍是一个开放性问题。基于重建的方法将图像通过一个在正常数据上训练的编码器-解码器网络，并根据图像与其重建结果之间的残差来检测异常。这仅在从编码器传递到解码器的信息受限时才有效；否则，网络会学习恒等映射，并同样重建异常。这种限制通常通过针对每个数据集调整的架构选择来施加，无法以比特为单位进行量化。我们引入了准二值化（QB）层，它将每个潜在元素压缩到[0,1]区间，并添加尺度为1/epsilon的拉普拉斯噪声。每个元素因此满足epsilon-局部差分隐私，且图像与其重建结果之间的互信息由一个仅依赖于epsilon和QB元素数量的量所界定，无论编码器和解码器如何。我们在每个编码器-解码器路径（包括所有跳跃连接）上放置QB层，构建了QBAE，一个具有32,768个QB元素的七层注意力U-Net。在MedIAnomaly基准的七个数据集上，QBAE使用单一架构和单一配置达到了0.828的平均图像级AUROC，这是不针对每个数据集进行调整的方法中的最高值，并且在BraTS2021上取得了最佳报告结果（AUROC 0.911，像素级AP 0.838）。噪声在测试时保留，因此每次重建都满足该界限。在没有输入损坏的情况下，仅瓶颈就防止了恒等崩溃（平均AUROC 0.805对比0.590）。代码可在以下网址获取：此https URL。
英文摘要
Unsupervised anomaly detection, which learns only from normal images, is a central task in medical image analysis and remains an open problem. Reconstruction-based methods pass an image through an encoder-decoder network trained on normal data and detect anomalies from the residual between the image and its reconstruction. This works only if the information passed from the encoder to the decoder is limited; otherwise the network learns an identity mapping and reconstructs anomalies too. This limit is usually imposed through architectural choices, tuned per dataset, that cannot be stated in bits. We introduce the quasi-binarizing (QB) layer, which squashes each latent element into [0, 1] and adds Laplace noise of scale 1/epsilon. Each element is then epsilon-locally differentially private, and the mutual information between an image and its reconstruction is bounded by a quantity that depends only on epsilon and the number of QB elements, whatever the encoder and decoder. Placing a QB layer on every encoder-decoder path, including all skip connections, we build QBAE, a seven-level attention U-Net with 32,768 QB elements. On the seven datasets of the MedIAnomaly benchmark, QBAE with one architecture and one configuration reaches a mean image-level AUROC of 0.828, the highest among methods that do not adapt to each dataset, and the best reported results on BraTS2021 (AUROC 0.911, pixel-level AP 0.838). The noise is kept at test time, so that every reconstruction satisfies the bound. Without input corruption, the bottleneck alone prevents identity collapse (mean AUROC 0.805 vs. 0.590). Code is available at https://github.com/hanaokalog/MedIAnomalyQB.
