PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks
Abstract
Psoriasis is a chronic skin condition that requires long-term treatment and monitoring. Although, the Psoriasis Area and Severity Index (PASI) is utilized as a standard measurement to assess psoriasis severity in clinical trials, it has many drawbacks such as (1) patient burden for in-person clinic visits for assessment of psoriasis, (2) time required for investigator scoring and (3) variability of inter- and intra-rater scoring. To address these drawbacks, we propose a novel and interpretable deep learning architecture called PSO-Net, which maps digital images from different anatomical regions to derive attention-based scores. Regional scores are further combined to estimate an absolute PASI score. Moreover, we devise a novel regression activation map for interpretability through ranking attention scores. Using this approach, we achieved inter-class correlation scores of 82.2% [95% CI: 77- 87%] and 87.8% [95% CI: 84-91%] with two different clinician raters, respectively.
Cite
@article{arxiv.2501.18782,
title = {PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks},
author = {Sharif A. Kamran and Molly V. Lucas and Brendon Lutnick and Chaitanya Parmar and Basudha Pal and Asha Patel Shah and David Apfel and Steven Fakharzadeh and Lloyd Miller and Stephen Yip and Kristopher Standish and Gabriela Oana Cula},
journal= {arXiv preprint arXiv:2501.18782},
year = {2025}
}
Comments
Accepted to IEEE ISBI 2025. 5 Pages, 3 figures, 2 tables