ArticleScientific reports2023
Psoriasis severity classification based on adaptive multi-scale features for multi-severity disease.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A review of psoriasis image analysis based on machine learning.Frontiers in medicine · 2024Pooled it
- Pink lotus flower (Biomedical reports · 2026Article
- Evaluating Risankizumab's Long-Term Effects in Psoriasis Using Optical Coherence Tomography.Dermatology and therapy · 2026Article
- Low molecular weight sulfated galactan attenuates imiquimod-induced psoriasis via suppression of proinflammatory cytokines and keratinocyte hyperproliferation.Biomedical reports · 2025Article
- Hard-to-Treat Areas in Psoriasis: An Underevaluated Part of the Disease.Life (Basel, Switzerland) · 2025Review
- Modifying the severity and appearance of psoriasis using deep learning to simulate anticipated improvements during treatment.Scientific reports · 2025Article
- Diffusion-based skin disease data augmentation with fine-grained detail preservation and interpolation for data diversity.PloS one · 2025Article
- A Hybrid Model for Psoriasis Subtype Classification: Integrating Multi Transfer Learning and Hard Voting Ensemble Models.Diagnostics (Basel, Switzerland) · 2024Article
- Parameter-based transfer learning for severity classification of atopic dermatitis using hyperspectral imaging.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
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4 authors.
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Abstract
Psoriasis is a skin disease that causes lesions of various sizes across the body and can persist for years with cyclic deterioration and improvement. During treatment, and a multiple-severity disease, with irregular severity within the observation area may be found. The current psoriasis evaluation is based on the subjective evaluation criteria of the clinician using the psoriasis area and severity index (PASI). We proposed a novel psoriasis evaluation method that detects representative regions as evaluation criteria, and extracts severity features to improve the evaluation performance of various types of psoriasis, including multiple-severity diseases. We generated multiple-severity disease images using CutMix and proposed a hierarchical multi-scale deformable attention module (MS-DAM) that can adaptively detect representative regions of irregular and complex patterns in multiple-severity disease analyses. EfficientNet B1 with MS-DAM exhibited the best classification performance with an F1-score of 0.93. Compared with the performance of the six existing self-attention methods, the proposed MS-DAM showed more than 5% higher accuracy than that of multiscale channel attention module (MS-CAM). Using the gradient-weighted activation mapping method, we confirmed that the proposed method works at par with human visual perception. We performed a more objective, effective, and accurate analysis of psoriasis severity using the proposed method.
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