Skip to main content

Table 5 Performance for the evaluated methods on the test data of the TCGA-PRAD dataset

From: Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification

Model

\(\kappa\)-GS

\(\kappa\)-PGP

\(\kappa\)-SGP

Avg. acc.

Error rate

Micro-precision

Supervised (100%)

0.30 ± 0.13

0.23 ± 0.16

0.10 ± 0.1

0.51 ± 0.06

2.45 ± 0.33

0.27 ± 0.05

Weakly supervised

0.49 ± 0.08

0.36 ± 0.11

0.30 ±0.09

0.67 ± 0.03

1.65 ± 0.18

0.43 ± 0.04

Fine-tuning (100%)

0.52 ± 0.05

0.34 ±0.10

0.40 ± 0.10

0.69 ±0.02

1.51 ± 0.14

0.46 ± 0.03

Back to article page

BMC Medical Imaging

ISSN: 1471-2342

Contact us