Computer Vision News - May 2022

45 Vision Transformers in Medical Computer Vision image patches of 16x16. They introduced simple numbers 1, 2, up to n as positional embeddings for specifying the positions of the patches. Vision transformers have the capability of modelling global context which assists in more accurate results. Medical images are considered as the input for vision transformers. For medical imaging classification the practitioners give their diagnosis by analyzing the medical images. Image classification has various applications in the medical domain. At present, image classification has various applications in the medical domain. Image classification using CNNs can be used for various applications. The applications were achieved through various CNN architectures such as AlexNet, VGGNet GoogleNet , ResNet. More resource-efficient architectures were proposed i.e. MobileNet, Squeeze and Excitation Net, and EfficientNet. Convolutional Neural Networks (CNNs) have been themost dominant deep neural networks for autonomous medical image analysis applications such as image classification during the last decade. These models, however, have shown poor performance in learning the long-range information, due to their localized receptive field. Transformer architecture, proposed by Vaswani et al., is currently the most popular model in the field of natural language processing (NLP). Self-attention modules are used in these models to learn the relationship between the embedded patches. In these models, the overall training process is predicated on dividing the input image into patches and considering each embedded patch as a word in NLP. They propose three different approaches to summarization. The performance matrices summarize the performance of each approach. They include a table