  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.
