Sparse Non-negative Matrix Factorization (SNMF) based color unmixing for breast histopathological image analysis

Xu, Jun; Xiang, Lei; Wang, Guanhao; Ganesan, Shridar; Feldman, Michael D.; Shih, Natalie; Gilmore, Hannah; Madabhushi, Anant

doi:10.1016/j.compmedimag.2015.04.002

Cited by 58 publications

(25 citation statements)

References 35 publications

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“…All the previously proposed methods were based off handcrafted features such as color and texture which aim to simulate the visual perception of human pathologist in interpreting the tissue samples [30]. Recently, however, there has been interested “deep learning” (DL) strategies for classification and analysis of big data.…”

Section: Previous Workmentioning

confidence: 99%

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images

et al. 2016

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Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions.

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Section: Previous Workmentioning

confidence: 99%

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images

et al. 2016

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“…As a common method, the purpose of the standard NMF is to find two non-negative matrices whose product is an optimal approximation to the original matrix (Sotiras et al, 2015 ; Xu et al, 2015 ). Therefore, the adjacency matrix Y ∈ R m * n can be decomposed into two parts after implementing NMF, namely, W ∈ R m * k and H ∈ R n * k ( Y ≈ WH T ).…”

Section: Methodsmentioning

confidence: 99%

Human Microbe-Disease Association Prediction With Graph Regularized Non-Negative Matrix Factorization

Peng

2018

Front. Microbiol.

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A microbe is a microscopic organism which may exists in its single-celled form or in a colony of cells. In recent years, accumulating researchers have been engaged in the field of uncovering microbe-disease associations since microbes are found to be closely related to the prevention, diagnosis, and treatment of many complex human diseases. As an effective supplement to the traditional experiment, more and more computational models based on various algorithms have been proposed for microbe-disease association prediction to improve efficiency and cost savings. In this work, we developed a novel predictive model of Graph Regularized Non-negative Matrix Factorization for Human Microbe-Disease Association prediction (GRNMFHMDA). Initially, microbe similarity and disease similarity were constructed on the basis of the symptom-based disease similarity and Gaussian interaction profile kernel similarity for microbes and diseases. Subsequently, it is worth noting that we utilized a preprocessing step in which unknown microbe-disease pairs were assigned associated likelihood scores to avoid the possible negative impact on the prediction performance. Finally, we implemented a graph regularized non-negative matrix factorization framework to identify potential associations for all diseases simultaneously. To assess the performance of our model, cross validations including global leave-one-out cross validation (LOOCV) and local LOOCV were implemented. The AUCs of 0.8715 (global LOOCV) and 0.7898 (local LOOCV) proved the reliable performance of our computational model. In addition, we carried out two types of case studies on three different human diseases to further analyze the prediction performance of GRNMFHMDA, in which most of the top 10 predicted disease-related microbes were verified by database HMDAD or experimental literatures.

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“…Other groups have presented different optimization approaches for color deconvolution [ 22 – 25 ]. However, these methods rely on assumptions that are not applicable to IHC images and/or do not consider the commonly used—and well validated—standard values and no validation in large data sets is available for these alternative approaches.…”

Section: Methodsmentioning

confidence: 99%

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images

et al. 2015

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BackgroundAccurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions.Methods and ResultsIn this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin—3,3’-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images.ValidationTo validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.ContextThus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.

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Sparse Non-negative Matrix Factorization (SNMF) based color unmixing for breast histopathological image analysis

Cited by 58 publications

References 35 publications

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images

Human Microbe-Disease Association Prediction With Graph Regularized Non-Negative Matrix Factorization

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images

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