Name
Title | ||
|---|---|---|
Ensemble Prostate Tumor Classification in H&E Whole Slide Imaging via Stain Normalization and Cell Density Estimation. |
Abstract | ||
|---|---|---|
Classification of prostate tumor regions in digital histology images requires comparable features across datasets. Here we introduce adaptive cell density estimation and apply H&E stain normalization into a supervised classification framework to improve inter-cohort classifier robustness. The framework uses Random Forest feature selection, class-balanced training example subsampling and support vector machine SVM classification to predict the presence of high- and low-grade prostate cancer HG-PCa and LG-PCa on image tiles. Using annotated whole-slide prostate digital pathology images to train and test on two separate patient cohorts, classification performance, as measured with area under the ROC curve AUC, was 0.703 for HG-PCa and 0.705 for LG-PCa. These results improve upon previous work and demonstrate the effectiveness of cell-density and stain normalization on classification of prostate digital slides across cohorts. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24888-2_34 | MLMI |
Field | DocType | Citations |
Normalization (statistics),Feature selection,Stain,Pattern recognition,Computer science,Support vector machine,Digital pathology,Robustness (computer science),Artificial intelligence,Classifier (linguistics),Random forest,Machine learning | Conference | 2 |
PageRank | References | Authors |
0.44 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michaela Weingant | 1 | 2 | 0.44 |
| hayley m reynolds | 2 | 5 | 1.92 |
| Annette Haworth | 3 | 3 | 1.12 |
| catherine mitchell | 4 | 4 | 1.24 |
| scott e williams | 5 | 4 | 1.24 |
| Matthew D. DiFranco | 6 | 22 | 2.36 |