Abstract
Psoriasis is a chronic skin disease which occurs to 2%–3% of the world’s entire population. If treated properly, patients can still maintain a relatively high quality of life. Otherwise, Psoriasis could cause severe complications or even threat to life. Therefore, continuous tracking of severity degree is critical in Psoriasis treatment. However, due to the shortage of dermatologists, it’s hard for patients to receive regular severity evaluation. Furthermore, evaluating the severity degree of Psoriasis is both time-consuming and error-prone which poses a heavy burden for dermatologists. To address this problem, we propose an automatic rating model which measures the severity degree quantitatively based on skin lesion pictures. The proposed rating model applies coarse to fine grained neural networks to evaluate skin lesions from multiple perspectives. According to experimental results, the proposed model outperforms experienced dermatologists.
X. Wu, Y. Yan, S. Zhao and Y. Kuang—Equal contribution.
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Acknowledgments
We sincerely thank all the anonymous reviewers and chairs for their constructive comments and suggestions that substantially improved this paper. This work was supported by National Key R&D Program of China, No. 2018YFC0117000.
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Wu, X. et al. (2021). Automatic Severity Rating for Improved Psoriasis Treatment. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_18
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