# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### Datasets
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Dataset** & **Paper** & **Type of Tissue** & **Size** & **Magnification** & **Labeled** & **Availability** \\ ICR14 e 12 & [59,71] & Breast & 163 images (WSI) (2000 \(\times\) 2000) & 40\(\times\) & Yes & Public \\ \hline Kather & [67] & Large intestine (colon and rectum) & 25,058 images (224 \(\times\) 224) & 20\(\times\) & Yes & Public \\ \hline KidneyArtPathology & [72] & Kidney & 5000 images (512 \(\times\) 512) & 20\(\times\) and 40\(\times\) & Yes & Public \\ \hline KIRC & [58,63,64,73] & Kidney & \textgreater{}190,000 images (WSI) (100,000 \(\times\) & 20\(\times\) & No & Public \\ \hline KPMP & [69] & Kidney & 85 images (WSI) & 40\(\times\) & Yes & Public \\ \hline Kumar & [58,64,65,67,74] & Breast, Liver, Kidney, Prostate, Bladder, Colon and Stomach & 30 images (1000 \(\times\) 1000) & 40\(\times\) & Yes & Public \\ \hline Lizard & [67] & Large intestine (colon and rectum) & 133 images (1016 \(\times\) 917) & 20\(\times\) & Yes & Public \\ \hline Lung dataset & [56] & Lung & 23,744 patchs & 40\(\times\) & - & Private \\ \hline LYON19 (HC) & [58] & Breast, colon, prostate & 871 images (256 \(\times\) 256) & 40\(\times\) & Yes & Public \\ \hline MICCAT16 GlaS & [68,70,75,76,77] & Large intestine (colon) & 165 images (775 \(\times\) 522) & 20\(\times\) & Yes & Public \\ \hline MITOS-ATYPLA 14 & [70,76] & Breast & 1200 images (1539 \(\times\) 1376) &
\begin{tabular}{l} 10\(\times\), 20\(\times\) and \\ 40\(\times\) \\ \end{tabular} & Yes & Public \\ \hline MoNuSAC & [63,78] & Lung, prostate, kidney and & 294 images (113 \(\times\) 81 and 1398 \(\times\) 1956) & 40\(\times\) & Yes & Public \\ \hline MoNuSeg & [63,68,79,80] & Prostate, Lung, Kidney, Colon, Breast, Pancares, Oral cavity, Stomach, Liver and Bladder & 44 images (1000 \(\times\) 1000) & 40\(\times\) & Yes & Public \\ \hline MTH & [58] & Colon & 36,000 images (256 \(\times\) 256) & 20\(\times\) and 40\(\times\) & Yes & Public \\ \hline MUC1 & [81] & Breast & 13,000 images (256 \(\times\) 266) & 40\(\times\) & No & Private \\ \hline N/E17, N5 & [82] & Kidney (mouse) & 1917 patchs (128 \(\times\) 128) & — & Private \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._ \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Dataset** & **Paper** & **Type of Tissue** & **Size** & **Magnification** & **Labeled** & **Availability** \\ \hline NSCLC & [83] & Lung & 269,000 pachs (128 \(\times\) 128) & 10\(\times\) & Yes & Public \\ \hline Onco-SG & [84] & Lung & 57 images (1792 \(\times\) 768) & 20\(\times\) & Yes & Public \\ \hline PanNuke & [62,67,80] & Breast, Lung, Prostate, Kidney, Brain, Colon and Liver & 7901 images (256 \(\times\) 256) & 20\(\times\) and 40\(\times\) & Yes & Public \\ \hline Roche & [85] & Liver (colorectal) & 50 images (WSI) (512 \(\times\) 512) & 10\(\times\) & & Private \\ \hline RWTH Aachen & [69] & Kidney & 1009 images (WSI) & 40\(\times\) & Yes & Private \\ \hline TCGA & [73,77,79,81,86,91,86,91,87,92] & Breast, Liver, Kidney, Prostate, Bladder, Brain, Colon and Stomach & \textgreater{}20,000 WSIs (100,000 \(\times\) 100,000) & 20\(\times\) and 40\(\times\) & No & Public \\ \hline TNBC & [58,63,64,74,79] & Breast & 50 images (512 \(\times\) 512) & 20\(\times\) and 40\(\times\) & Yes & Public \\ \hline IUPAC16 & [70,71,77] & Breast & 821 images (WSI) & 40\(\times\) & Yes & Private \\ \hline VALIGA & [69] & Kidney & 648 images (WSI) & 40\(\times\) & Yes & Private \\ \hline - & [92] & Liver & 64, 128, 256, 512 e 1024 patchs & 40\(\times\) & Yes & Private \\ \hline - & [93] & Colon and Kidney & 100 images (WSI) & — & Yes & Private \\ \hline - & [94] & Kidney & 10 images & 40\(\times\) & Yes & Private \\ \hline - & [95] & Prostate and Colon & 102 images (WSI) (1024 \(\times\) 1024) & 10\(\times\) & Yes & Public \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._