# Application of Artificial Intelligence in the Practice of Medicine
## 2 Review of Issue Contents
Finally, U-Net was used to segment the cerebral infarction region using the CycleGAN-generated images. Regarding extraction accuracy, the U-Net-with-CycleGAN images showed an improvement over those of U-Net without CycleGAN, were efficient, and assisted in extracting the infarction area accurately while maintaining the detection rate. STHarDNet [8] is a novel segmentation model for magnetic resonance imaging (MRI). In MRI segmentation, conventional approaches utilize U-Net models with encoder-decoder structures, segmentation models using vision transformers, or models that combine a vision transformer with an encoder-decoder model structure. However, conventional models are large with low computation speeds, and, in vision transformer models, the amount of computation sharply increases with the image size. To overcome these problems, the STHarDNet model is proposed, which combines Swin transformer blocks and a lightweight U-Net-type model that has a HarDNet block-based encoder-decoder structure. To maintain the features of the hierarchical transformer and shifted windows approach of the Swin transformer model, the Swin transformer is used in the first skip connection layer of the encoder, instead of in the encoder-decoder bottleneck. STHarDNet improved the accuracy and speed of MRI image-based stroke diagnosis. In general, combined, the Swin transformer blocks and lightweight U-Net type model maintained the advantage of hierarchical feature extraction and demonstrated excellent segmentation performance. The Swin transformer restricts the computation of attention to each window, and this also maintains high calculation speeds. The whole-slide image (WSI) is a digitized medical image. Processing WSIs to train neural networks is often intricate and labor-intensive. [PERSON] et al. [9] developed an open-source library dealing with recurrent tasks in the processing of WSIs and helped with the training and evaluation of neuronal networks for classification tasks. First, a large WSI is divided into multiple small tiles. Thereafter, the region of interest (ROI) is extracted using a filtering algorithm that stores each WSI's dimensions, ROI, and tile information. In addition, evaluations are available at each level while preserving the hierarchical structure. Neural network training continues using the fastai library, which applies filtered information for learning, reduces storage space, and increases the processing speed. This approach supplements the clinicopathological diagnoses of brain tumors. Upper gastrointestinal endoscopy is widely performed to detect early gastric cancers (GCs). The automated detection of early GCs from endoscopic images involves an object detection model. However, the reduction of false positives involves challenges in the detected results. [PERSON] et al. [10] propose an object detection model, U-Net R-CNN, based on a semantic segmentation technique that extracts target objects by performing local analysis on the images. The candidate regions were extracted using U-Net; however, many regions were over-detected in the detected candidate regions. Therefore, the candidate region was cut and input to the CNN to classify the candidate region as a GC or a false positive. Finally, the regions identified by the CNN were considered candidate regions. DerseNet169 was used as the convolutional neural network for box classification, which improved the detection performance compared with the previous method. In [11] the authors verified that adversarial attacks were not negligible during open-source development. Open-source deep neural networks (DNNs) for medical imaging are significant in emergent situations, such as during the COVID-19 pandemic because they accelerate the development of high-performance DNN-based systems. The COVID-Net model, an open-source DNN model for detecting COVID-19 from chest X-ray images, is susceptible to backdoor attacks that modify DNN models and cause misclassification when a specific input trigger is added.