Due to the lack of efficient mpox diagnostic technology, mpox cases continue to increase. Recently, the great potential of deep learning models in detecting mpox and non-mpox has been proven. However, existing models learn image representations via image classification, which results in they may be easily susceptible to interference from real-world noise, require diverse non-mpox images, and fail to detect abnormal input. These drawbacks make classification models inapplicable in real-world settings. To address these challenges, we propose "Mask, Inpainting, and Measure" (MIM). In MIM's pipeline, a generative adversarial network only learns mpox image representations by inpainting the masked mpox images. Then, MIM determines whether the input belongs to mpox by measuring the similarity between the inpainted image and the original image. The underlying intuition is that since MIM solely models mpox images, it struggles to accurately inpaint non-mpox images in real-world settings. Without utilizing any non-mpox images, MIM cleverly detects mpox and non-mpox and can handle abnormal inputs. We used the recognized mpox dataset (MSLD) and images of eighteen non-mpox skin diseases to verify the effectiveness and robustness of MIM. Experimental results show that the average AUROC of MIM achieves 0.8237. In addition, we demonstrated the drawbacks of classification models and buttressed the potential of MIM through clinical validation. Finally, we developed an online smartphone app to provide free testing to the public in affected areas. This work first employs generative models to improve mpox detection and provides new insights into binary decision-making tasks in medical images.

翻译：针对猴痘诊断技术匮乏的现状，猴痘病例持续攀升。近期深度学习模型在猴痘与非猴痘检测领域展现出巨大潜力。然而，现有模型通过图像分类学习图像表征，导致其易受真实环境噪声干扰、需要多样化非猴痘图像且无法检测异常输入。这些缺陷使得分类模型难以应用于实际场景。为应对上述挑战，我们提出"掩膜-修复-度量"（MIM）框架。在MIM流程中，生成对抗网络仅通过修复掩膜后的猴痘图像来学习猴痘图像表征，随后通过度量修复图像与原图的相似性判定输入是否属于猴痘类别。其核心逻辑在于：由于MIM仅对猴痘图像建模，因此在真实场景中难以准确修复非猴痘图像。无需使用任何非猴痘图像，MIM即可智能区分猴痘与非猴痘病例，并能处理异常输入。我们采用公认的猴痘数据集（MSLD）及十八种非猴痘皮肤病变图像验证了MIM的有效性与鲁棒性。实验结果显示，MIM的平均AUROC值达到0.8237。此外，通过临床验证我们证实了分类模型的局限性及其对MIM潜力的支撑作用。最终，我们开发了在线智能手机应用程序，为疫区公众提供免费检测服务。本研究首次运用生成模型提升猴痘检测效能，并为医学图像二元决策任务提供了新思路。