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10.4103/2153-3539.179984 http://scihub22266oqcxt.onion/10.4103/2153-3539.179984 C4837797!4837797!27141322     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Pathol+Inform 2016 ; 7 (ä): ä Nephropedia Template TP {{tp|p=27141322|t=2016. Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images |pdf=|usr=}}{{27141322}} gab.com Text Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images JO: J Pathol Inform http://www.kidney.de/pget.php?&tw=1&pmid=27141322 #FOAvip Context:: Color normalization techniques for histology have not been empirically tested for their utility for computational pathology pipelines. Aims:: We compared two contemporary techniques for achieving a common intermediate goal ? epithelial-stromal classification. Settings and Design:: Expert-annotated regions of epithelium and stroma were treated as ground truth for comparing classifiers on original and color-normalized images. Materials and Methods:: Epithelial and stromal regions were annotated on thirty diverse-appearing H and E stained prostate cancer tissue microarray cores. Corresponding sets of thirty images each were generated using the two color normalization techniques. Color metrics were compared for original and color-normalized images. Separate epithelial-stromal classifiers were trained and compared on test images. Main analyses were conducted using a multiresolution segmentation (MRS) approach; comparative analyses using two other classification approaches (convolutional neural network [CNN], Wndchrm) were also performed. Statistical Analysis:: For the main MRS method, which relied on classification of super-pixels, the number of variables used was reduced using backward elimination without compromising accuracy, and test - area under the curves (AUCs) were compared for original and normalized images. For CNN and Wndchrm, pixel classification test-AUCs were compared. Results:: Khan method reduced color saturation while Vahadane reduced hue variance. Super-pixel-level test-AUC for MRS was 0.010?0.025 (95% confidence interval limits ± 0.004) higher for the two normalized image sets compared to the original in the 10?80 variable range. Improvement in pixel classification accuracy was also observed for CNN and Wndchrm for color-normalized images. Conclusions:: Color normalization can give a small incremental benefit when a super-pixel-based classification method is used with features that perform implicit color normalization while the gain is higher for patch-based classification methods for classifying epithelium versus stroma. Twit Text FOAVip Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images ????J Pathol Inform http://www.kidney.de/pget.php?&tw=1&pmid=27141322 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27141322 #FOAvip English Wikipedia <ref>{{Cite pmid|27141322}}</ref> Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images #MMPMID27141322 Sethi A; Sha L; Vahadane AR; Deaton RJ; Kumar N; Macias V; Gann PH J Pathol Inform 2016[]; 7 (ä): ä PMID27141322show ga Context:: Color normalization techniques for histology have not been empirically tested for their utility for computational pathology pipelines. Aims:: We compared two contemporary techniques for achieving a common intermediate goal ? epithelial-stromal classification. Settings and Design:: Expert-annotated regions of epithelium and stroma were treated as ground truth for comparing classifiers on original and color-normalized images. Materials and Methods:: Epithelial and stromal regions were annotated on thirty diverse-appearing H and E stained prostate cancer tissue microarray cores. Corresponding sets of thirty images each were generated using the two color normalization techniques. Color metrics were compared for original and color-normalized images. Separate epithelial-stromal classifiers were trained and compared on test images. Main analyses were conducted using a multiresolution segmentation (MRS) approach; comparative analyses using two other classification approaches (convolutional neural network [CNN], Wndchrm) were also performed. Statistical Analysis:: For the main MRS method, which relied on classification of super-pixels, the number of variables used was reduced using backward elimination without compromising accuracy, and test - area under the curves (AUCs) were compared for original and normalized images. For CNN and Wndchrm, pixel classification test-AUCs were compared. Results:: Khan method reduced color saturation while Vahadane reduced hue variance. Super-pixel-level test-AUC for MRS was 0.010?0.025 (95% confidence interval limits ± 0.004) higher for the two normalized image sets compared to the original in the 10?80 variable range. Improvement in pixel classification accuracy was also observed for CNN and Wndchrm for color-normalized images. Conclusions:: Color normalization can give a small incremental benefit when a super-pixel-based classification method is used with features that perform implicit color normalization while the gain is higher for patch-based classification methods for classifying epithelium versus stroma. äEmpirical comparison of color normalization methods for epithelial-str(epmc).pdf DeepDyve Pubget Overpricing