@article {303,
	title = {When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections.},
	journal = {Med Image Anal},
	volume = {35},
	year = {2017},
	month = {2017 01},
	pages = {530-543},
	abstract = {<p>Classification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models of genomics and clinical outcome. To tackle the large technical variations and biological heterogeneities, which are intrinsic in large cohorts, emerging systems utilize either prior knowledge from pathologists or unsupervised feature learning for invariant representation of the underlying properties in the data. However, to a large degree, the architecture for tissue histology classification remains unexplored and requires urgent systematical investigation. This paper is the first attempt to provide insights into three fundamental questions in tissue histology classification: I. Is unsupervised feature learning preferable to human engineered features? II. Does cellular saliency help? III. Does the sparse feature encoder contribute to recognition? We show that (a) in I, both Cellular Morphometric Feature and features from unsupervised feature learning lead to superior performance when compared to SIFT and [Color, Texture]; (b) in II, cellular saliency incorporation impairs the performance for systems built upon pixel-/patch-level features; and (c) in III, the effect of the sparse feature encoder is correlated with the robustness of features, and the performance can be consistently improved by the multi-stage extension of systems built upon both Cellular Morphmetric Feature and features from unsupervised feature learning. These insights are validated with two cohorts of Glioblastoma Multiforme (GBM) and Kidney Clear Cell Carcinoma (KIRC).</p>
},
	keywords = {Color, Histological Techniques, Humans, Pattern Recognition, Automated, Unsupervised Machine Learning},
	issn = {1361-8423},
	doi = {10.1016/j.media.2016.08.010},
	author = {Zhong, Cheng and Han, Ju and Borowsky, Alexander and Parvin, Bahram and Wang, Yunfu and Hang Chang}
}
@article {87,
	title = {Molecular predictors of 3D morphogenesis by breast cancer cell lines in 3D culture},
	journal = {PLoS Comput Biol},
	volume = {6},
	year = {2010},
	month = {2010 Feb},
	pages = {e1000684},
	abstract = {<p>Correlative analysis of molecular markers with phenotypic signatures is the simplest model for hypothesis generation. In this paper, a panel of 24 breast cell lines was grown in 3D culture, their morphology was imaged through phase contrast microscopy, and computational methods were developed to segment and represent each colony at multiple dimensions. Subsequently, subpopulations from these morphological responses were identified through consensus clustering to reveal three clusters of round, grape-like, and stellate phenotypes. In some cases, cell lines with particular pathobiological phenotypes clustered together (e.g., ERBB2 amplified cell lines sharing the same morphometric properties as the grape-like phenotype). Next, associations with molecular features were realized through (i) differential analysis within each morphological cluster, and (ii) regression analysis across the entire panel of cell lines. In both cases, the dominant genes that are predictive of the morphological signatures were identified. Specifically, PPARgamma has been associated with the invasive stellate morphological phenotype, which corresponds to triple-negative pathobiology. PPARgamma has been validated through two supporting biological assays.</p>},
	keywords = {Biological, Biomarkers, Breast Neoplasms, Cell Culture Techniques, Cell Line, Computer-Assisted, ErbB-2, Female, Gene Expression Profiling, Histocytochemistry, Humans, Image Processing, Models, Phenotype, PPAR gamma, Receptor, Reproducibility of Results, Tumor},
	issn = {1553-7358},
	doi = {10.1371/journal.pcbi.1000684},
	author = {Han, Ju and Hang Chang and Giricz, Orsi and Lee, Genee Y and Baehner, Frederick L and Gray, Joe W and Bissell, Mina J and Kenny, Paraic A and Parvin, Bahram}
}
