发表机构
Avalytiq（Avalytiq）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对远程皮肤病学病例分级性评估瓶颈，提出语义三视图管道，基于SCIN数据集训练DeepLabV3+模型，结合逻辑回归分类器实现可解释的自动分级筛查，性能优于基线，可过滤不可分级照片。
AI 中文摘要
智能手机皮肤照片是远程皮肤病学的必备工具，但评估提交病例的诊断适用性（分级性）仍是移动护理工作流程中的关键瓶颈。皮肤科医生通常会查看多个摄影视图（区域视图、角度视图和特写视图）以识别一致的纹理细节，而非依赖单张图像。我们提出了语义三视图管道（Semantic Tri-view Pipeline），这是一种用于自动远程皮肤病学分级性筛查的可解释架构，将表皮微浮雕形式化为可计算的图像质量生物标志物。使用公共SCIN数据集的专家注释子集，我们训练了轻量级DeepLabV3+模型以分割微浮雕保真度。随后，将这些空间掩码在多达三个病例视图上进行聚合，结合逻辑回归分类器，利用视角冗余性在不受控的智能手机采集条件下支持鲁棒性。该方法学习上下文感知、临床可理解的启发式规则，例如对区域距离视图中的高保真纹理进行惩罚。在预定义的90%灵敏度操作点进行评估时，系统的明显误差主要反映了临床医生依赖非视觉元数据的 borderline 病例中的主观临床差异。在SCIN数据集上，性能从差异较大的多数共识病例的AUC 0.81（阳性预测值PPV为80.6%）提升至光学明确的一致病例的AUC 0.96（PPV为97.7%）。总体而言，本研究提供了一种可解释、注重隐私、适用于边缘设备的系统，可在病例提交期间提供实时反馈，以在审核前过滤不可分级的照片集。
英文摘要
Smartphone skin photographs are indispensable to teledermatology, yet assessing the diagnostic suitability of submitted cases (gradability) remains a critical bottleneck in mobile care workflows. Dermatologists routinely review multiple photographic views (regional, angled, and close-up) to identify consistent textural detail rather than relying on a single image. We present the Semantic Tri-view Pipeline, an interpretable architecture for automated teledermatology gradability screening that formalizes epidermal micro-relief as a computable biomarker of image quality. Using an expert-annotated subset of the public SCIN dataset, we train a lightweight DeepLabV3+ model to segment micro-relief fidelity. These spatial masks are then aggregated across up to three case views with a logistic regression classifier, leveraging viewpoint redundancy to support robustness under uncontrolled smartphone acquisition. This approach learns context-aware, clinically intelligible heuristics, such as penalizing high-fidelity texture in regional distance views. Evaluated at a predefined 90% sensitivity operating point, the system's apparent errors largely reflect subjective clinical variance on borderline cases where clinicians rely on non-visual metadata. On SCIN, performance improves from an AUC of 0.81 (80.6% PPV) on variance-heavy majority-consensus cases to 0.96 (97.7% PPV) on optically unambiguous unanimous cases. Overall, this work delivers an interpretable, privacy-by-design, edge-ready system that can provide real-time feedback during case submission to filter ungradable photo sets before review.
Journal ref2026 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Pittsburgh, PA, USA, 2026, pp. 469-474
DOI：10.1109/CHASE69719.2026.00080
