首页> Computers in biology and medicine> Sickle-cell disease diagnosis support selecting the most appropriate machine learning method: Towards a general and interpretable approach for cell morphology analysis from microscopy images.

Sickle-cell disease diagnosis support selecting the most appropriate machine learning method: Towards a general and interpretable approach for cell morphology analysis from microscopy images.

导出 LinkOut

翻译打印收藏纠错

第一作者： Nataša,Petrović

第一单位： UGiVIA Research Group, University of the Balearic Islands, Dpt. of Mathematics and Computer Science, Crta. Valldemossa, Km 7.5, E-07122, Palma, Spain. Electronic address: npe785@uib.es.

作者： Nataša,Petrović ^[1] ; Gabriel,Moyà-Alcover ^[2] ; Antoni,Jaume-I-Capó ^[3] ; Manuel,González-Hidalgo ^[4]

作者单位： UGiVIA Research Group, University of the Balearic Islands, Dpt. of Mathematics and Computer Science, Crta. Valldemossa, Km 7.5, E-07122, Palma, Spain. Electronic address: npe785@uib.es. ^[1] UGiVIA Research Group, University of the Balearic Islands, Dpt. of Mathematics and Computer Science, Crta. Valldemossa, Km 7.5, E-07122, Palma, Spain. Electronic address: gabriel.moya@uib.es. ^[2] UGiVIA Research Group, University of the Balearic Islands, Dpt. of Mathematics and Computer Science, Crta. Valldemossa, Km 7.5, E-07122, Palma, Spain. Electronic address: antoni.jaume@uib.es. ^[3] SCOPIA Research Group, University of the Balearic Islands, Dpt. of Mathematics and Computer Science, Crta. Valldemossa, Km 7.5, E-07122, Palma, Spain; Health Research Institute of the Balearic Islands (IdISBa), E-07010, Palma, Spain. Electronic address: manuel.gonzalez@uib.es. ^[4]

关键词 Interpretability Machine learning Microscopy image Morphology analysis Red blood cell Sickle-cell disease

医学主题词贫血, 镰状细胞性(Anemia, Sickle Cell);人类(Humans);显微镜检查(Microscopy)

DOI 10.1016/j.compbiomed.2020.104027

PMID 33075715

发布时间 2021-06-21

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交