Minimizing the need for pixel-level annotated data for training PET anomaly segmentation networks is crucial, particularly due to time and cost constraints related to expert annotations. Current un-/weakly-supervised anomaly detection methods rely on autoencoder or generative adversarial networks trained only on healthy data, although these are more challenging to train. In this work, we present a weakly supervised and Implicitly guided COuNterfactual diffusion model for Detecting Anomalies in PET images, branded as IgCONDA-PET. The training is conditioned on image class labels (healthy vs. unhealthy) along with implicit guidance to generate counterfactuals for an unhealthy image with anomalies. The counterfactual generation process synthesizes the healthy counterpart for a given unhealthy image, and the difference between the two facilitates the identification of anomaly locations. The code is available at: https://github.com/igcondapet/IgCONDA-PET.git

翻译：减少像素级标注数据对于训练PET异常分割网络的需求至关重要，特别是考虑到专家标注在时间和成本上的限制。当前的弱监督/无监督异常检测方法依赖于仅在健康数据上训练的自编码器或生成对抗网络，但这些方法训练难度较大。在本工作中，我们提出了一种弱监督的隐式引导反事实扩散模型用于PET图像异常检测，命名为IgCONDA-PET。该模型的训练以图像类别标签（健康与非健康）为条件，并结合隐式引导，为存在异常的非健康图像生成反事实样本。反事实生成过程为给定的非健康图像合成其对应的健康版本，两者之间的差异有助于识别异常位置。代码获取地址：https://github.com/igcondapet/IgCONDA-PET.git