Unsupervised anomaly segmentation approaches to pathology segmentation train
a model on images of healthy subjects, that they define as the 'normal' data
distribution. At inference, they aim to segment any pathologies in new images
as 'anomalies', as they exhibit patterns that deviate from those in 'normal'
training data. Prevailing methods follow the 'corrupt-and-reconstruct'
paradigm. They intentionally corrupt an input image, reconstruct it to follow
the learned 'normal' distribution, and subsequently segment anomalies based on
reconstruction error. Corrupting an input image, however, inevitably leads to
suboptimal reconstruction even of normal regions, causing false positives. To
alleviate this, we propose a novel iterative spatial mask-refining strategy
IterMask2. We iteratively mask areas of the image, reconstruct them, and update
the mask based on reconstruction error. This iterative process progressively
adds information about areas that are confidently normal as per the model. The
increasing content guides reconstruction of nearby masked areas, improving
reconstruction of normal tissue under these areas, reducing false positives. We
also use high-frequency image content as an auxiliary input to provide
additional structural information for masked areas. This further improves
reconstruction error of normal in comparison to anomalous areas, facilitating
segmentation of the latter. We conduct experiments on several brain lesion
datasets and demonstrate effectiveness of our method. Code is available at:
this https URL

给定一篇研究论文，提取出五个关键词，准确代表其主要主题和研究领域。然后，用简化的中文单句来总结论文。

IterMask2: MRI 中脑部病变的迭代非监督异常分割方法基于空间和频率掩蔽

IterMask2: Iterative Unsupervised Anomaly Segmentation via Spatial and  Frequency Masking for Brain Lesions in MRI

Unsupervised anomaly segmentation aims to detect patterns that are distinct
from any patterns processed during training, commonly called abnormal or
out-of-distribution patterns, without providing any associated manual
segmentations. Since anomalies during deployment can lead to model failure,
detecting the anomaly can enhance the reliability of models, which is valuable
in high-risk domains like medical imaging. This paper introduces Masked
Modality Cycles with Conditional Diffusion (MMCCD), a method that enables
segmentation of anomalies across diverse patterns in multimodal MRI. The method
is based on two fundamental ideas. First, we propose the use of cyclic modality
translation as a mechanism for enabling abnormality detection.
Image-translation models learn tissue-specific modality mappings, which are
characteristic of tissue physiology. Thus, these learned mappings fail to
translate tissues or image patterns that have never been encountered during
training, and the error enables their segmentation. Furthermore, we combine
image translation with a masked conditional diffusion model, which attempts to
`imagine' what tissue exists under a masked area, further exposing unknown
patterns as the generative model fails to recreate them. We evaluate our method
on a proxy task by training on healthy-looking slices of BraTS2021
multi-modality MRIs and testing on slices with tumors. We show that our method
compares favorably to previous unsupervised approaches based on image
reconstruction and denoising with autoencoders and diffusion models.

该研究介绍了一种名为 Masked Modality Cycles with Conditional Diffusion (MMCCD) 的方法，用于在多模式 MRI 中分割异常区域，通过结合循环模态转换和掩码条件扩散模型，实现了对训练过程中未遇到的组织和图像样式的异常检测。该方法在医学图像领域具有重要的应用价值。

基于掩码条件扩散的模态循环用于 MRI 无监督异常分割

Modality Cycles with Masked Conditional Diffusion for Unsupervised  Anomaly Segmentation in MRI

We present the efficiency of semi-orthogonal embedding for unsupervised
anomaly segmentation. The multi-scale features from pre-trained CNNs are
recently used for the localized Mahalanobis distances with significant
performance. However, the increased feature size is problematic to scale up to
the bigger CNNs, since it requires the batch-inverse of multi-dimensional
covariance tensor. Here, we generalize an ad-hoc method, random feature
selection, into semi-orthogonal embedding for robust approximation, cubically
reducing the computational cost for the inverse of multi-dimensional covariance
tensor. With the scrutiny of ablation studies, the proposed method achieves a
new state-of-the-art with significant margins for the MVTec AD, KolektorSDD,
KolektorSDD2, and mSTC datasets. The theoretical and empirical analyses offer
insights and verification of our straightforward yet cost-effective approach.

本文介绍了一种半正交嵌入模型，用于无监督异常分割任务。该模型使用预训练卷积神经网络中的多尺度特征来计算局部马氏距离，使用随机特征选择的方式对多维协方差张量进行逼近，从而极大地降低了计算开销，实验结果表明该方法在多个数据集上表现显著优于现有最优解。

半正交嵌入用于高效无监督异常分割

Semi-orthogonal Embedding for Efficient Unsupervised Anomaly  Segmentation

Brain pathologies can vary greatly in size and shape, ranging from few pixels
(i.e. MS lesions) to large, space-occupying tumors. Recently proposed
Autoencoder-based methods for unsupervised anomaly segmentation in brain MRI
have shown promising performance, but face difficulties in modeling
distributions with high fidelity, which is crucial for accurate delineation of
particularly small lesions. Here, similar to these previous works, we model the
distribution of healthy brain MRI to localize pathologies from erroneous
reconstructions. However, to achieve improved reconstruction fidelity at higher
resolutions, we learn to compress and reconstruct different frequency bands of
healthy brain MRI using the laplacian pyramid. In a range of experiments
comparing our method to different State-of-the-Art approaches on three
different brain MR datasets with MS lesions and tumors, we show improved
anomaly segmentation performance and the general capability to obtain much more
crisp reconstructions of input data at native resolution. The modeling of the
laplacian pyramid further enables the delineation and aggregation of lesions at
multiple scales, which allows to effectively cope with different pathologies
and lesion sizes using a single model.

使用拉普拉斯金字塔对健康脑 MRI 的不同频带进行压缩和重建来提高重构保真度，实现对大、小脑病变的定位和聚合，并且表现出优异的异常分割效果。