# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### Datasets  
The studies included in this review used a variety of datasets to train and evaluate GAN-based models applied to histological image segmentation. The Table 3 provides a comprehensive overview of the datasets employed in histopathological image studies. It offers a structured analysis based on tissue type, image size, magnification, annotation, and availability criteria. First, various tissue types are represented across the datasets, supporting the development of generalizable models. Dataset sizes range from fewer than 100 to hundreds of thousands of images, with resolutions spanning small patches to full whole-slide images (WSIs). Most images were acquired at 20\(\times\) or 40\(\times\) magnification, critical for capturing histological detail, though some datasets lack magnification metadata, which may hinder reuse. High-quality annotations are available in many datasets, enabling precise model training, though some (e.g., TCGA, KIRC) lack detailed labels, restricting their use to unsupervised or semi-supervised approaches. Public availability of most datasets promotes reproducibility and algorithm benchmarking, but access to some remains restricted, limiting transparency and broader validation in clinical settings. Figure 5: Annual distribution on GAN-based research. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dataset** & **Paper** & **Type of Tissue** & **Size** & **Magnification** & **Labeled** & **Availability** \\ \hline ANHIR & [56] & Kidney, breast, colon, lung and gastric mucosa & 231 images (WSI) (100,000 \(\times\) 200,000) & 20\(\times\) and 40\(\times\) & Yes & Public \\ \hline BBBC-data & [57, 58] & Liver, Brain, Colon, Lung, Ovary, Kidney, Blood, Immune System, Embryo, Heart and Nervous & 700 images & 20\(\times\) & Yes & Public \\  & & System & & & & \\ \hline BMCD-FGCD & [59] & Bone marrow & 29 images (WSI) (1500 \(\times\) 800) & — & Yes & Public \\ \hline CAMELYON16 & [60] & Breast (lymph nodes) & 400 images (WSI) (100,000 \(\times\) 200,000) & 20\(\times\) and 40\(\times\) & Yes & Public \\ \hline CARS & [61] & Thyroid & 200 images (256 \(\times\) 256) & 40\(\times\) & Yes & Private \\ \hline CoNIC & [62] & Colon & 4981 patches (256 \(\times\) 256) & — & Yes & Public \\ \hline CoNSep & [58, 63, 64] & Adrenal, Larynx, Lymph node, Mediastium. & 30 images (512 \(\times\) 512) & 40\(\times\) & Yes & Public \\ \hline CPMI7 & [58, 63, 64, 65] & Colon & 64 images (500 \(\times\) 500) & 20\(\times\) and 40\(\times\) & Yes & Public \\ \hline Crag & [66, 67] & Colorectal carcinoma & 213 images (1512 \(\times\) 1512) & 20\(\times\) & Yes & Public \\ \hline CryNuSeg & [63] &
\begin{tabular}{l} Adrenal gland, larynx, lymph \\ node, mediastum, pancreas, \\ pleura, testis, thymus and \\ Thyroid gland \\ \end{tabular} & 30 images (512 \(\times\) 512) & 20\(\times\) & Yes & Public \\ \hline Digestpath & [66] & Colorectal carcinoma & 46 images (5000 \(\times\) 5000) & 20\(\times\) & Yes & Public \\ \hline Glomeruli & [56] & Kidney & 200 images (WSI) (1000 \(\times\) 1000) & 40\(\times\) & Yes & Public \\ \hline HN Cancer & [68] & Head and Neck & 1562 images (1024 \(\times\) 1280) & 10\(\times\) & Yes & Private \\ \hline HuBMAP & [69] & Kidney & 9 images (WSI) & 40\(\times\) & Yes & Public \\ \hline ICIAR-BACH & [70] & Breast & 400 images (2048 \(\times\) 1536) & 20\(\times\) & Yes & Public \\ \hline In-House & [67] & Colorectal & 1093 images (WSI) (512 \(\times\) 512) & 40\(\times\) & Yes & Public \\ \hline In-House-MIL & [67] & Kidney, Lung, Breast, Prostate, Endomet & 260 images (WSI) (10,000 \(\times\) 10,000) & 40\(\times\) & Yes & Public \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of the datasets used in the included studies.