Data from: Computational pathology to discriminate benign from malignant intraductal proliferations of the breast

Dong F, Irshad H, Oh E, Lerwill MF, Brachtel EF, Jones NC, Knoblauch NW, Montaser-Kouhsari L, Johnson NB, Rao LKF, Faulkner-Jones B, Wilbur DC, Schnitt SJ, Beck AH

Date Published: December 10, 2014

DOI: http://dx.doi.org/10.5061/dryad.pv85m

 

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Title Data: Original Images
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Description This contains two data sets (MGH - Training and BIDMC - Evaluation).
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Title Data: Segmented Images
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Description This contains nuclei segmented images.
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Title Data: Original and Segmented Images
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Description This contains both original and segmented images.
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Title Fig: Analysis Figures
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Description This contains analysis figures that describes the framework performance on data sets.
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Title Code: Texture Features Computation
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Description This is C++ code that computes intensity and texture features of each segmented nuclei. This code requires ITK 4 or above version and Boost library to compile and run. It also requires a library of color transformation into different color spaces which you can find at this link (https://github.com/midas-journal/midas-journal-780.git).
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Title Code: Nuclei Segmentation & Morphological Features
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Description This contains a Fiji (ImageJ) Macro that segment nuclei and compute morphological Features.
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Title File: Selected Features List
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Description This file contains a list of selected features.
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Title File: Computed Features with Class Label
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Description This file contains all computed features including Morphological, intensity and textural features with class label.
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Title File: Breast Cancer Cases (UDH & DCIS)
Downloaded 41 times
Description This file contains a list of all cases with clinical data that used for class labelling.
Download BreastCancerCases_UDH_DCIS_167.xls (92.16 Kb)
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Title Code: Analysis in R
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Description This code used computed features as input and generate analysis figures as output that described the framework performance.
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When using this data, please cite the original publication:

Dong F, Irshad H, Oh E, Lerwill MF, Brachtel EF, Jones NC, Knoblauch NW, Montaser-Kouhsari L, Johnson NB, Rao LKF, Faulkner-Jones B, Wilbur DC, Schnitt SJ, Beck AH (2014) Computational pathology to discriminate benign from malignant intraductal proliferations of the breast. PLoS ONE 9(12): e114885. http://dx.doi.org/10.1371/journal.pone.0114885

Additionally, please cite the Dryad data package:

Dong F, Irshad H, Oh E, Lerwill MF, Brachtel EF, Jones NC, Knoblauch NW, Montaser-Kouhsari L, Johnson NB, Rao LKF, Faulkner-Jones B, Wilbur DC, Schnitt SJ, Beck AH (2014) Data from: Computational pathology to discriminate benign from malignant intraductal proliferations of the breast. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.pv85m
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