皮肤病深度学习诊断模型的研究进展

[中图分类号] R751 [文献标志码] A

ABSTRACTSkin diseases significantly affect the quality of life of approximately 19O million individuals worldwide.Thecomplexityand diversity of theirclinical manifestationsare the majorchalenges for traditional diagnostic approaches，and exploring novel diagnostic strategies has become an urgent priority. In recentyears，deep learming（DL）technology has been increasinglyapplied in the intelligent recognition of skin diseases，demonstrating substantial potential. This study provides a systematic review of the research progress of DL in dermatological diagnosis from three major dimensions.First，at the data input level，it focuses on the characterization and preprocessing of multimodal data， including dermoscopicimages，ultrasound images，and histopathological slides. Second，at the algorithmic model level，it explores ensemble learning frameworks，multimodal data fusion strategies，multicenter collaborative training approaches，and interpretable model construction. Finally，at the task recognition level，it evaluates the performance of DL models in benign skin disease screening，malignant skin lesion diferentiation， and binary as well as multiclass classification tasks. By comprehensively reviewing advancements in DL-based skin disease diagnostic models from multiple perspectives，this paper aims to provide valuable insights for the further optimization and clinical translation of inteligent diagnostic systems.

KEYWORDsDeep learning；Skin diseases；Input data；Intelligent identification；Dermoscopicimages； Ultrasound images

皮肤作为人体最大的器官，其相关疾病影响范围广泛，不仅显著影响患者生活质量，部分疾病甚至危及患者生命。（剩余18061字）