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Deep learning for microscopic examination of protozoan parasites.
Zhang, Chi; Jiang, Hao; Jiang, Hanlin; Xi, Hui; Chen, Baodong; Liu, Yubing; Juhas, Mario; Li, Junyi; Zhang, Yang.
Afiliação
  • Zhang C; College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.
  • Jiang H; College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.
  • Jiang H; College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.
  • Xi H; College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.
  • Chen B; Department of Neurosurgery, Shenzhen Hospital of Peking University, Shenzhen, Guangdong, China.
  • Liu Y; Department of Thoracic Surgery, Huazhong University of Science and Technology Union Shenzhen Hospital, Guangdong, China.
  • Juhas M; Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland.
  • Li J; School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.
  • Zhang Y; College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.
Comput Struct Biotechnol J ; 20: 1036-1043, 2022.
Article em En | MEDLINE | ID: mdl-35284048
ABSTRACT
The infectious and parasitic diseases represent a major threat to public health and are among the main causes of morbidity and mortality. The complex and divergent life cycles of parasites present major difficulties associated with the diagnosis of these organisms by microscopic examination. Deep learning has shown extraordinary performance in biomedical image analysis including various parasites diagnosis in the past few years. Here we summarize advances of deep learning in the field of protozoan parasites microscopic examination, focusing on publicly available microscopic image datasets of protozoan parasites. In the end, we summarize the challenges and future trends, which deep learning faces in protozoan parasite diagnosis.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Comput Struct Biotechnol J Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Comput Struct Biotechnol J Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China