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Med Image Anal. 2017 Jan;35:530-543. doi: 10.1016/j.media.2016.08.010. Epub 2016 Sep 9.

When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections.

Author information

1
Lawrence Berkeley National Laboratory, Berkeley CA USA.
2
Center for Comparative Medicine, University of California, Davis,CA, USA.
3
Department of Electrical and Biomedical Engineering, University of Nevada, Reno, NV USA.
4
Lawrence Berkeley National Laboratory, Berkeley CA USA; Department of Neurology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
5
Lawrence Berkeley National Laboratory, Berkeley CA USA. Electronic address: hchang@lbl.gov.

Abstract

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).

KEYWORDS:

Classification; Computational histopathology; Sparse feature encoder; Unsupervised feature learning

PMID:
27644083
PMCID:
PMC5099087
DOI:
10.1016/j.media.2016.08.010
[Indexed for MEDLINE]
Free PMC Article

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