Cargando…
Content-based image retrieval of digitized histopathology in boosted spectrally embedded spaces
CONTEXT: Content-based image retrieval (CBIR) systems allow for retrieval of images from within a database that are similar in visual content to a query image. This is useful for digital pathology, where text-based descriptors alone might be inadequate to accurately describe image content. By repres...
Autores principales: | Sridhar, Akshay, Doyle, Scott, Madabhushi, Anant |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Medknow Publications & Media Pvt Ltd
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4498317/ https://www.ncbi.nlm.nih.gov/pubmed/26167385 http://dx.doi.org/10.4103/2153-3539.159441 |
Ejemplares similares
-
Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images
por: Sparks, Rachel, et al.
Publicado: (2016) -
Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation
por: Li, Lin, et al.
Publicado: (2017) -
An active learning based classification strategy for the minority class problem: application to histopathology annotation
por: Doyle, Scott, et al.
Publicado: (2011) -
Content-based histopathology image retrieval using CometCloud
por: Qi, Xin, et al.
Publicado: (2014) -
Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer
por: Doyle, Scott, et al.
Publicado: (2012)