# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### GANs in Histological Image
#### 5.4.2 Stain Normalization
\begin{table}
\begin{tabular}{l l l l l} \hline
**Work** & **Task** & **Model** & **Setting** & **Performance** \\ \hline [PERSON], [PERSON] and [PERSON] (2022) [57] & Data augmentation & GAN & GPU Tesla V100, CPU Intel Xeon 6230, & - \\  & & & 128 GB RAM & \\ \hline [PERSON] et al. (2022) [79] & Data augmentation & CSinGAN & NVIDIA Tesla V100 & - \\ \hline [PERSON] and [PERSON] (2022) [90] & Color normalization & LSGAN & - & \(NMI=0.800\), \(BICC=0.750,W\)\(\mathrm{scc}=0.590\) \\ \hline [PERSON] et al. (2019) [88] & Stain normalization & cGAN & \begin{tabular}{l} Intel Xeon E5-269 v4, \\ 256 GB of RAM, \\ NVIDIA Tesla V100 GPU \\ \end{tabular} & \begin{tabular}{l} - \\ - \\ 128 GB RAM \\ \end{tabular} \\ \hline [PERSON] et al. (2022) [92] & Data augmentation, Color normalization, Domain transformation & cGAN & \begin{tabular}{l} cGAN \\ \(HC=0.75\pm 0.13,BCD=0.31\pm 0.06\), \\ CycleGAN \\ \end{tabular} & - & \begin{tabular}{l} cGAN: \(MMI=0.24\pm 0.08,NMI=0.09\pm 0.03\), \\ \(HC=0.61\pm 0.26,BCD=0.35\pm 0.15\) \\ \end{tabular} \\ \hline [PERSON] et al. (2024) & Stain normalization & CycleGAN & \begin{tabular}{l} GPU NVIDIA Tesla V100, \\ 16 GB de memoria, \\ Intel Xeon CPU E5-2680 v4 \\ \end{tabular} & \begin{tabular}{l} FID: ChLoS = 82.44, \\ FID: MoNuSeg = 15.32, \\ FID: ThScar = 8.00, \\ FID: ChCombine = 7.63 \\ \end{tabular} \\ \hline [PERSON] et al. (2022) [71] & Data augmentation & cGAN & \begin{tabular}{l} GPU NVIDIA 2080 T, \\ 11 GB RAM \\ \end{tabular} & - \\ \hline [PERSON] et al. (2023) [90] & Color Normalization & Restore-GAN & \begin{tabular}{l} GPU NVIDIA P100, \\ 16 GB RAM \\ \end{tabular} & \begin{tabular}{l} \(SSIM=0.949\pm 0.020\), \\ \(PSNM=30.678\pm 1.624\) \\ \end{tabular} \\ \hline [PERSON] et al. (2025) [78] & Synthetic image generation & ViT-P2P & - & - \\ \hline [PERSON] et al. (2023) [67] & Stain normalization & CycleGAN & \
\begin{tabular}{l} Nvidia RTX 309 GPU, \\ 24 GB of GPU memory, \\ Intel Xeon, 64 GB of RAM \\ \end{tabular} & - \\ \hline [PERSON] and \\ [PERSON] (2017) [75] & Stain Normalization & GAN & NVIDIA Titan X GPU &
\begin{tabular}{l} _MITOSIS-IS_ = 0.700, \\ _COLON-IS_ = 0.950, \\ _OVAK-IS_ = 0.260 \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 4: _Cnt._ \begin{table}
\begin{tabular}{l l l l l} \hline
**Work** & **Task** & **Model** & **Setting** & **Performance** \\ \hline [PERSON] et al. (2021) [94] & Data augmentation & CycleGAN, & - & \begin{tabular}{l} _CycleGAN_: \(FID=5.359\), \\ _StarGAN_: \(FID=11.572\) \\ \end{tabular} \\ \hline [PERSON] et al. (2023) [72] & Data augmentation & CycleGAN & - & \begin{tabular}{l} \(FID=56.00,SSIM=0.81\) \\ \end{tabular} \\ \hline [PERSON] et al. (2021) [82] & Stain normalization & \begin{tabular}{l} ACCP-GAN \\ \end{tabular} & \begin{tabular}{l} GPU NVIDIA RTX, \\ CPU Intel Xeon Silver 4110, \\ 128 GB RAM \\ \end{tabular} & \begin{tabular}{l} \(FSTM=0.9657,MS-SSIM=0.9694\), \\ _PSNR_ = \(27.25085,VIF=0.9031\), \\ _FID_ = \(27.1231\) \\ \end{tabular} \\ \hline [PERSON] et al. (2022) [80] & Data augmentation & GAN & \begin{tabular}{l} NVIDIA GTX 1070, \\ 16 GB RAM \\ \end{tabular} & - \\ \hline [PERSON] et al. (2025) [60] & Stain normalization & SASSL & - & - \\ \hline [PERSON] et al. (2024) [74] & Virtual staining & \begin{tabular}{l} CUT E-CUT \\ CycleGAN \\ E-CycleGAN \\ \end{tabular} & \begin{tabular}{l} GPU NVIDIA RTX 3090 \\ AMD EPYC 7302 CPU, \\ 346 GB of RAM \\ \end{tabular} & \begin{tabular}{l} FID-CycleGAN: 67.26, E-CycleGAN: 61.91, \\ _CUT_: 54.87, E-CUT: 50.91 \\ \end{tabular} \\ \hline [PERSON] et al. (2021) [64] & Data augmentation & MASG-GAN & \begin{tabular}{l} GPU NVIDIA RTX 2080 Ti, \\ CPU Intel (TM) i9-10850 K, \\ 32 GB RAM \\ \end{tabular} & - \\ \hline [PERSON] et al. (2022) [88] & Color normalization & \begin{tabular}{l} CSTN \\ and Virtual staining \\ \end{tabular} & \
\begin{tabular}{l} NVIDIA RTX 3090 GPU \\ \end{tabular} &
\begin{tabular}{l} \(FID=101.37\), \\ \(KID=6.129,AHR=1.98\) \\ \end{tabular} \\ \hline \end{tabular}
\end{table}