Quanti.us: a tool for rapid, flexible, crowd-based annotation of images.
Nat Methods
; 15(8): 587-590, 2018 08.
Article
en En
| MEDLINE
| ID: mdl-30065368
ABSTRACT
We describe Quanti.us , a crowd-based image-annotation platform that provides an accurate alternative to computational algorithms for difficult image-analysis problems. We used Quanti.us for a variety of medium-throughput image-analysis tasks and achieved 10-50× savings in analysis time compared with that required for the same task by a single expert annotator. We show equivalent deep learning performance for Quanti.us-derived and expert-derived annotations, which should allow scalable integration with tailored machine learning algorithms.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Programas Informáticos
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Nat Methods
Asunto de la revista:
TECNICAS E PROCEDIMENTOS DE LABORATORIO
Año:
2018
Tipo del documento:
Article
País de afiliación:
Estados Unidos