Name
Abstract | ||
|---|---|---|
Motivation: Tumor tile selection is a necessary prerequisite in patch-based cancer whole slide image analysis, which is labor-intensive and requires expertise. Whole slides are annotated as tumor or tumor free, but tiles within a tumor slide are not. As all tiles within a tumor free slide are tumor free, these can be used to capture tumor-free patterns using the one-class learning strategy. Results: We present a Python package, termed OCTID, which combines a pretrained convolutional neural network (CNN) model, Uniform Manifold Approximation and Projection (UMAP) and one-class support vector machine to achieve accurate tumor tile classification using a training set of tumor free tiles. Benchmarking experiments on four H&E image datasets achieved remarkable performance in terms of F1-score (0.900.06), Matthews correlation coefficient (0.93 +/- 0.05) and accuracy (0.94 +/- 0.03). |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1093/bioinformatics/btab416 | BIOINFORMATICS |
DocType | Volume | Issue |
Journal | 37 | 21 |
ISSN | Citations | PageRank |
1367-4803 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanan Wang | 1 | 0 | 0.34 |
| Litao Yang | 2 | 0 | 0.34 |
| Geoffrey I. Webb | 3 | 3130 | 234.10 |
| zongyuan ge | 4 | 149 | 27.83 |
| Jiangning Song | 5 | 374 | 41.93 |