AI-powered microscopy image analysis for parasitology: integrating human expertise.

Feng, Ruijun; Li, Sen; Zhang, Yang

Feng, Ruijun; Li, Sen; Zhang, Yang.

Afiliación

Feng R; College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China; School of Computer Science and Engineering, University of New South Wales, Sydney, Australia.
Li S; College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.
Zhang Y; College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China. Electronic address: zhangyang07@hit.edu.cn.

Trends Parasitol ; 40(7): 633-646, 2024 Jul.

Article en En | MEDLINE | ID: mdl-38824067

ABSTRACT

ABSTRACT

Microscopy image analysis plays a pivotal role in parasitology research. Deep learning (DL), a subset of artificial intelligence (AI), has garnered significant attention. However, traditional DL-based methods for general purposes are data-driven, often lacking explainability due to their black-box nature and sparse instructional resources. To address these challenges, this article presents a comprehensive review of recent advancements in knowledge-integrated DL models tailored for microscopy image analysis in parasitology. The massive amounts of human expert knowledge from parasitologists can enhance the accuracy and explainability of AI-driven decisions. It is expected that the adoption of knowledge-integrated DL models will open up a wide range of applications in the field of parasitology.

Asunto(s)

Inteligencia Artificial; Procesamiento de Imagen Asistido por Computador; Microscopía; Parasitología; Parasitología/métodos; Parasitología/instrumentación; Parasitología/tendencias; Microscopía/instrumentación; Microscopía/métodos; Microscopía/normas; Humanos; Procesamiento de Imagen Asistido por Computador/instrumentación; Procesamiento de Imagen Asistido por Computador/métodos; Aprendizaje Profundo

Palabras clave

artificial intelligence; deep learning; human expert knowledge; knowledge integration; knowledge representation; microscopy image analysis

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Parasitología / Procesamiento de Imagen Asistido por Computador / Inteligencia Artificial / Microscopía Límite: Humans Idioma: En Revista: Trends Parasitol Asunto de la revista: PARASITOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido

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