传统机器学习与自动化机器学习的比较分析：推进倒置性乳头状瘤与相关鳞状细胞癌的诊断
Comparative analysis of traditional machine learning and automated machine learning: advancing inverted papilloma versus associated squamous cell carcinoma diagnosis.
作者信息
Hosseinzadeh Farideh, Mohammadi S Saeed, Palmer James N, Kohanski Michael A, Adappa Nithin D, Chang Michael T, Hwang Peter H, Nayak Jayakar V, Patel Zara M
机构信息
Department of Otolaryngology-Head & Neck Surgery, Stanford University School of Medicine, Stanford, California, USA.
Byers Eye Institute, Department of Ophthalmology, Stanford University, Palo Alto, California, USA.
出版信息
Int Forum Allergy Rhinol. 2024 Dec;14(12):1957-1960. doi: 10.1002/alr.23438. Epub 2024 Aug 26.
Inverted papilloma conversion to squamous cell carcinoma is not always easy to predict. AutoML requires much less technical knowledge and skill to use than traditional ML. AutoML surpassed the traditional ML algorithm in differentiating IP from IP-SCC.
内翻性乳头状瘤转变为鳞状细胞癌并不总是易于预测。与传统机器学习相比，自动机器学习的使用所需的技术知识和技能要少得多。在区分内翻性乳头状瘤与内翻性乳头状瘤相关鳞状细胞癌方面，自动机器学习超越了传统机器学习算法。
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