@article{discovery1575672,
       publisher = {NATURE PUBLISHING GROUP},
            year = {2016},
           title = {Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images},
         journal = {Scientific Reports},
           month = {June},
          volume = {6},
            note = {This work is licensed under a Creative Commons Attribution 4.0 International License. The images
or other third party material in this article are included in the article's Creative Commons license,
unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license,
users will need to obtain permission from the license holder to reproduce the material. To view a copy of this
license, visit http://creativecommons.org/licenses/by/4.0/},
             url = {http://doi.org/10.1038/srep27306},
        abstract = {Content-based image retrieval (CBIR) retrieves database images most similar to the query image by (1)
extracting quantitative image descriptors and (2) calculating similarity between database and query
image descriptors. Recently, manifold learning (ML) has been used to perform CBIR in a low dimensional
representation of the high dimensional image descriptor space to avoid the curse of dimensionality.
ML schemes are computationally expensive, requiring an eigenvalue decomposition (EVD) for every
new query image to learn its low dimensional representation. We present out-of-sample extrapolation
utilizing semi-supervised ML (OSE-SSL) to learn the low dimensional representation without
recomputing the EVD for each query image. OSE-SSL incorporates semantic information, partial class
label, into a ML scheme such that the low dimensional representation co-localizes semantically similar
images. In the context of prostate histopathology, gland morphology is an integral component of
the Gleason score which enables discrimination between prostate cancer aggressiveness. Images are
represented by shape features extracted from the prostate gland. CBIR with OSE-SSL for prostate
histology obtained from 58 patient studies, yielded an area under the precision recall curve (AUPRC) of
0.53{$\pm$}0.03 comparatively a CBIR with Principal Component Analysis (PCA) to learn a low dimensional
space yielded an AUPRC of 0.44{$\pm$}0.01.},
            issn = {2045-2322},
          author = {Sparks, R and Madabhushi, A}
}