@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 = {

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 = {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 {102, title = {Phenotypic Characterization of Breast Invasive Carcinoma via Transferable Tissue Morphometric Patterns learned from Glioblastoma Multiforme.}, journal = {Proc IEEE Int Symp Biomed Imaging}, volume = {2016}, year = {2016}, month = {2016 Apr}, pages = {1025-1028}, abstract = {

Quantitative analysis of whole slide images (WSIs) in a large cohort may provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. Although unsupervised feature learning provides a promising way in learning pertinent features without human intervention, its capability can be greatly limited due to the lack of well-curated examples. In this paper, we explored the transferability of knowledge acquired from a well-curated Glioblastoma Multiforme (GBM) dataset through its application to the representation and characterization of tissue histology from the Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) cohort. Our experimental results reveals two major phenotypic subtypes with statistically significantly different survival curves. Further differential expression analysis of these two subtypes indicates enrichment of genes regulated by NF-kB in response to TNF and genes up-regulated in response to IFNG.

}, issn = {1945-7928}, doi = {10.1109/ISBI.2016.7493440}, author = {Han, Ju and Fontenay, Gerald V and Wang, Yunfu and Jiang-Hua Mao and Hang Chang} } @article {242, title = {When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections.}, journal = {Med Image Anal}, volume = {35}, year = {2016}, month = {2016 Sep 9}, pages = {530-543}, 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).

}, 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} }