Early and accurate disease detection is crucial for patient management and successful treatment outcomes. However, the automatic identification of anomalies in medical images can be challenging. Conventional methods rely on large labeled datasets which are difficult to obtain. To overcome these limitations, we introduce a novel unsupervised approach, called PHANES (Pseudo Healthy generative networks for ANomaly Segmentation). Our method has the capability of reversing anomalies, i.e., preserving healthy tissue and replacing anomalous regions with pseudo-healthy (PH) reconstructions. Unlike recent diffusion models, our method does not rely on a learned noise distribution nor does it introduce random alterations to the entire image. Instead, we use latent generative networks to create masks around possible anomalies, which are refined using inpainting generative networks. We demonstrate the effectiveness of PHANES in detecting stroke lesions in T1w brain MRI datasets and show significant improvements over state-of-the-art (SOTA) methods. We believe that our proposed framework will open new avenues for interpretable, fast, and accurate anomaly segmentation with the potential to support various clinical-oriented downstream tasks.

翻译：早期准确的疾病检测对于患者管理和治疗成功至关重要。然而，医学图像中异常的自动识别可能具有挑战性。传统方法依赖于难以获取的大规模标注数据集。为克服这些限制，我们提出一种名为PHANES（伪健康生成网络用于异常分割）的新型无监督方法。我们的方法具有逆转异常的能力，即保留健康组织并用伪健康重建替代异常区域。与近期扩散模型不同，我们的方法既不依赖于学习到的噪声分布，也不对整幅图像引入随机改动。相反，我们利用潜在生成网络为可能异常区域创建掩码，并通过修复生成网络对其进行优化。我们展示了PHANES在T1加权脑部MRI数据集中检测卒中病灶的有效性，并证明其显著优于现有最优方法。我们相信所提出的框架将为可解释、快速且精准的异常分割开辟新途径，并具有支持各种临床导向下游任务的潜力。