Table 22 Cross-domain generalization evaluation. Significant values are in bold.

From: Novel dual gland GAN architecture improves human protein localization classification using salivary and pituitary gland inspired loss functions

Dataset/modality

Cell Types

Imaging technique

Original performance

With dual-gland GAN

Improvement

p-value

Fluorescence microscopy

Human protein atlas

17 types

Confocal fluorescence

85.2% accuracy

92.6% accuracy

 + 7.4%

 < 0.001

Cell painting dataset

12 types

High-content imaging

82.7% accuracy

89.8% accuracy

 + 7.1%

 < 0.001

Allen cell collection

8 types

Live cell imaging

81.4% accuracy

88.3% accuracy

 + 6.9%

 < 0.001

Brightfield microscopy

DIC cellular Dataset

15 types

Differential interference

78.9% accuracy

85.1% accuracy

 + 6.2%

 < 0.001

Phase contrast collection

10 types

Phase contrast

77.3% accuracy

83.7% accuracy

 + 6.4%

 < 0.001

Specialized modalities

Electron microscopy (EM)

6 types

Transmission EM

74.6% accuracy

80.8% accuracy

 + 6.2%

 < 0.001

Multi-photon Dataset

8 types

Two-photon microscopy

79.2% accuracy

85.9% accuracy

 + 6.7%

 < 0.001

Super-resolution (STORM)

5 types

Stochastic optical

76.8% accuracy

82.4% accuracy

 + 5.6%

0.002

Cross-species validation

Mouse cell atlas

14 types

Confocal fluorescence

80.3% accuracy

87.1% accuracy

 + 6.8%

 < 0.001

Drosophila cellular DB

9 types

Fluorescence imaging

79.7% accuracy

85.9% accuracy

 + 6.2%

 < 0.001

Yeast cell collection

7 types

Brightfield/fluorescence

75.4% accuracy

81.2% accuracy

 + 5.8%

0.001

Clinical/pathological

Cancer cell lines

20 types

Various modalities

83.1% accuracy

89.7% accuracy

 + 6.6%

 < 0.001

Histopathology dataset

12 types

H&E staining

81.9% accuracy

87.8% accuracy

 + 5.9%

 < 0.001

Average performance

12.4 types

Mixed modalities

79.8% accuracy

86.4% accuracy

 + 6.6%

 < 0.001