# SA-GAN: Stain Aclimation Generative Adversarial Network for Histopathology Image Analysis
## 4 Experimental Results  
In this section, the proposed SA-GAN and other stain normalization methods designed from different aspects are compared and discussed. The compared state-of-the-art methods are implemented in python, running on an Intel(r) Xeon(r) CPU E5-1620 v3 PC with 3.54 GHz CPU with one NVIDIA Tesla M40 GPU of 12 GB memory. However, for the implementation of the proposed SA-GAN model, the PyTorch library was used. To optimize the generator and discriminators of the SA-GAN model, we applied the Adam optimizer using the batch size of 1. We \(\alpha\) = 0.001, \(\beta\) = 0.01, and \(\gamma\) = 0.01, so that two discriminators can perform a major role in the training of feature extractors, while minorly taking part in training of generator. The whole model was trained using a learning rate = 0.0002. For experimental analysis, three publicly available breast cancer [33; 34; 35] and one colon cancer [5] datasets were used. The detailed description of datasets is given as follows:  
**The mitos & amp; atypia 14 (MITOS-ATYPIA-14) challenge dataset**[33]: This dataset includes 1200 training and 496 test HPF images. Annotations of training data are available but annotations of test data are withheld by organizers. The HPF images were scanned with two AperioXT and Hammatsu Nanozoomer 2.0-HT scanners at \(\times\)40 magnifications. We checked the model performance on HPFs that are scanned by AperioXT scanner. The size of these images is 1539 \(\times\) 1376 pixels. The training and test sets have larger appearance variability in images in terms of texture contents and staining properties. **The tumor proliferation assessment challenge (TUPAC 2016) challenge dataset**[35]: The auxiliary dataset contains images of 73 breast cancer patients arising from three different pathology centers. Among 73 training cases, the first 23 correspond to the previously released AMIDA13 contest dataset [36]. These cases are obtained from the pathology center at the university medical department in Utrecht. The remaining 50 cases come from the other two different pathology labs in the Netherlands. These images were produced with the Leica SCN400 scanner at \(\times\)40 magnification with spatial resolution of 0.25 \(\upmu\)m/pixel. The annotations were labeled by two different pathologists. The annotations of training data are publicly available while annotations of test data are not yet available. **The ICIAR 2018 breast cancer histology (BACH) grand challenge dataset**[34]: This dataset is provided as a part of international conference on image analysis and recognition (ICIAR 2018) breast cancer histology challenge. It contains 400 training and 100 test H&E-stained microscopy images of 2048 \(\times\) 1536 \(\times\) 3-pixel resolution. Images are scanned by LeicaDM 2000 LED microscope of 0.42 \(\times\) 0.42 \(\upmu\)m/pixel resolution. These images were labeled by two expert pathologists into four classes. Labels of training images are available; however, labels of test images are withheld by the organizers. Large color variability exists in this data, so this dataset is more appropriate for the color normalization task and to evaluate the performance of automatic cancer diagnostic systems. We used this dataset to perform the multiclass classification of breast histopathology images: normal, benign, in situ, and invasive carcinoma classes. **MICCAI'16 gland segmentation (GlaS) challenge dataset**[5]: This dataset is provided as a part of international conference on medical image computing and computer assisted intervention (MICCAI 2016) gland segmentation challenge. The training dataset contains 85 H&E colon adenocarcinoma tissue images, 37 belong to benign tumors, and the remaining 48 belong to malignant tumors category. The test data consist of 80 test images; 37 belong to benign tumors and 43 to malignant tumors. The spatial resolution of these images is 775 \(\times\) 522 pixels, which were scanned by Zeiss MIRAX MIDI scanner of 20\(\times\) (0.62005 \(\upmu\)m/pixels) magnification.