OpenHI: open platform for histopathological image annotation Online publication date: Thu, 01-Aug-2019
by Pargorn Puttapirat; Haichuan Zhang; Jingyi Deng; Yuxin Dong; Jiangbo Shi; Peiliang Lou; Chunbao Wang; Lixia Yao; Xiangrong Zhang; Chen Li
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 22, No. 4, 2019
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.
Online publication date: Thu, 01-Aug-2019
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