Paper Info
Title
Openhi: Open Platform For Histopathological Image Annotation
Abstract
Consolidating semantically rich annotation on digital histopathological images known as whole-slide images requires a software capable of handling such type of biomedical data with support for procedures which align with existing pathological protocols. Demands for large-scale annotated histopathological datasets are on the raise since they are needed for developments of artificial intelligence techniques to promote automated diagnosis, mass screening, phenotype-genotype association study, etc. This paper presents an open platform for efficient collaborative histopathological image annotation with standardised semantic enrichment at a pixel-level precision named OpenHI (Open Histopathological Image). The framework's responsive processing algorithm can perform large-scale histopathological image annotation and serve as biomedical data infrastructure for digital pathology. Its web-based design is highly configurable and could be extended to annotate histopathological image of various oncological types. The framework is open-source and fully documented.
Year
DOI
Venue
2019
10.1504/IJDMB.2019.101393
INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
Keywords
Field
DocType
OpenHI, digital pathology, WSI, whole-slide image, image annotation, virtual slide, virtual magnification, histopathology, cancer diagnosis, cancer grading, genotype-phenotype association
Automatic image annotation,Open platform,Information retrieval,Computer science,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
22
4
1748-5673
Citations 
PageRank 
References 
0
0.34
0
Authors
10
Name
Order
Citations
PageRank
Pargorn Puttapirat100.68
Haichuan Zhang202.70
Jingyi Deng300.34
Yuxin Dong401.01
Jiangbo Shi500.34
Peiliang Lou600.68
Chunbao Wang713.75
Lixia Yao8459.63
Xiangrong Zhang949348.70
Chen Li108054.64