用可解释深度学习自动评估银屑病严重程度，减少医生评分差异。
PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks
- 基于注意力机制的网络，从不同身体部位图像生成评分
- 与两位医生评分相关性达82.2%和87.8%
- 生成注意力热图，直观展示判断依据，适合临床辅助
银屑病是一种需要长期治疗与监测的慢性皮肤病。尽管临床试验中常使用银屑病面积与严重程度指数（PASI）作为评估标准，但其存在诸多缺陷：（1）患者需频繁赴诊，负担重；（2）医师评分耗时；（3）评分者间及同一评分者内差异大。为此，我们提出一种新型可解释深度学习架构PSO-Net，通过分析不同解剖区域的数字图像，生成基于注意力的局部评分，并融合为整体PASI得分。同时，设计了一种新的回归激活图以增强可解释性，通过排序注意力分数实现判读依据可视化。实验显示，该方法与两位不同医师评分的相关系数分别为82.2% [95% CI: 77–87%] 和87.8% [95% CI: 84–91%]。
原文摘要 · Abstract (English)
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.
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