Copyright
©The Author(s) 2020.
Artif Intell Cancer. Jun 28, 2020; 1(1): 31-38
Published online Jun 28, 2020. doi: 10.35713/aic.v1.i1.31
Published online Jun 28, 2020. doi: 10.35713/aic.v1.i1.31
Table 1 Concordance of classification results between the paired digital pathology images
| The second scan | ||||
| Positive | Negative | Unclassifiable | ||
| The first scan | Positive | 248 | 66 | 0 |
| Negative | 69 | 197 | 2 | |
| Unclassifiable | 1 | 0 | 4 | |
- Citation: Ogura M, Kiyuna T, Yoshida H. Impact of blurs on machine-learning aided digital pathology image analysis. Artif Intell Cancer 2020; 1(1): 31-38
- URL: https://www.wjgnet.com/2644-3228/full/v1/i1/31.htm
- DOI: https://dx.doi.org/10.35713/aic.v1.i1.31