AutoPaint：一种用于无监督异常检测的自修复方法
AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection
AI总结：
本文提出AutoPaint自修复方法，通过学习健康解剖结构并自动替换肿瘤外观，在PET、CT等多模态数据上实现了优于最新UAD方法且媲美监督分割的无监督异常检测。
摘要：
使用监督方法稳健且准确地检测和分割出现在不同解剖器官中的异质性肿瘤，需要覆盖所有可能疾病类型的大规模标注数据集。由于此类丰富数据集不可用且标注成本高昂，无监督异常检测（UAD）方法被开发出来，旨在通过利用未标注的健康图像数据，将病理检测为对正常状态的偏离。然而，已开发的UAD模型通常使用不完整的健康解剖结构分布进行训练，并且在保持解剖约束方面存在困难。本工作首先提出一个稳健的修复模型，以学习健康解剖结构的细节，并通过保持解剖约束来重建高分辨率图像。其次，我们提出一种自动修复流水线，以自动检测肿瘤，用学习到的健康解剖结构替换其外观，并基于此以纯无监督的方式分割肿瘤体积。三个成像数据集，包括肺部肿瘤和头颈部肿瘤的PET、CT和PET-CT扫描，被作为评估基准进行研究。实验结果表明，所提出的方法相对于广泛的最新UAD方法具有显著的优越性。此外，我们提出的无监督方法在应用于多模态图像时，产生了与稳健的监督分割方法相当的结果。
英文摘要：
Robust and accurate detection and segmentation of heterogenous tumors appearing in different anatomical organs with supervised methods require large-scale labeled datasets covering all possible types of diseases. Due to the unavailability of such rich datasets and the high cost of annotations, unsupervised anomaly detection (UAD) methods have been developed aiming to detect the pathologies as deviation from the normality by utilizing the unlabeled healthy image data. However, developed UAD models are often trained with an incomplete distribution of healthy anatomies and have difficulties in preserving anatomical constraints. This work intends to, first, propose a robust inpainting model to learn the details of healthy anatomies and reconstruct high-resolution images by preserving anatomical constraints. Second, we propose an autoinpainting pipeline to automatically detect tumors, replace their appearance with the learned healthy anatomies, and based on that segment the tumoral volumes in a purely unsupervised fashion. Three imaging datasets, including PET, CT, and PET-CT scans of lung tumors and head and neck tumors, are studied as benchmarks for evaluation. Experimental results demonstrate the significant superiority of the proposed method over a wide range of state-of-the-art UAD methods. Moreover, the unsupervised method we propose produces comparable results to a robust supervised segmentation method when applied to multimodal images.
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。
