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
The second scan | ||||
Positive | Negative | Unclassifiable | ||
The first scan | Positive | 248 | 66 | 0 |
Negative | 69 | 197 | 2 | |
Unclassifiable | 1 | 0 | 4 |
Color channel | Median of the non-flipped group | Median of the flipped group | P value |
R | 0.0350 ± 0.0220 | 0.0347 ± 0.0217 | 0.900 |
G | 0.0319 ± 0.0197 | 0.0313 ± 0.0205 | 0.931 |
B | 0.0266 ± 0.0148 | 0.0250 ± 0.0190 | 0.255 |
- 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