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Due to the lack of efficient mpox diagnostic technology, mpox cases continueto increase. Recently, the great potential of deep learning models in detectingmpox and non-mpox has been proven. However, existing models learn imagerepresentations via image classification, which results in they may be easilysusceptible to interference from real-world noise, require diverse non-mpoximages, and fail to detect abnormal input. These drawbacks make classificationmodels inapplicable in real-world settings. To address these challenges, wepropose "Mask, Inpainting, and Measure" (MIM). In MIM's pipeline, a generativeadversarial network only learns mpox image representations by inpainting themasked mpox images. Then, MIM determines whether the input belongs to mpox bymeasuring the similarity between the inpainted image and the original image.The underlying intuition is that since MIM solely models mpox images, itstruggles to accurately inpaint non-mpox images in real-world settings. Withoututilizing any non-mpox images, MIM cleverly detects mpox and non-mpox and canhandle abnormal inputs. We used the recognized mpox dataset (MSLD) and imagesof eighteen non-mpox skin diseases to verify the effectiveness and robustnessof MIM. Experimental results show that the average AUROC of MIM achieves0.8237. In addition, we demonstrated the drawbacks of classification models andbuttressed the potential of MIM through clinical validation. Finally, wedeveloped an online smartphone app to provide free testing to the public inaffected areas. This work first employs generative models to improve mpoxdetection and provides new insights into binary decision-making tasks inmedical images.

Adversarial Masked Image Inpainting for Robust Detection of Mpox and Non-Mpox