Computer Vision News - May 2022

44 AI Research differentiating between white matter and grey, in brain. There is a specialized MRI called functional Magnetic Resonance Imaging (fMRI), which is used for observing brain structure and locating the areas of the brain which are activated during cognitive tasks. Ultrasound can be used to internal organs within the body, noninvasively. Ultrasound images are captured in 2D, 3D, but it can also capture 4D images which is 3D in motion such as a heart beating or blood flowing through blood vessels. The Whole-Slide Imaging (WSI) refers to capturing the microscopic tissue specimens from a glass slide of biopsy or surgical specimen which results in high-resolution digitized images. Specimens on glass slides transformed into high-resolution digital files can be efficiently stored, accessed, analyzed, and shared with scientists from across the web using slide management technologies. WSI is changing the workflows of many laboratories. PET scans canbeused for cancer detectionanddiagnosis. PET scans canbeused todetermine spread of the cancer, determining the recurrence of cancer, metastasis, evaluating brain abnormalities like tumor and memory disorder. PET scans map normal human brain and heart function. AI and healthcare analysts can use deep learning concepts, techniques, and architectures to bridge the gap between them. Deep neural networks in computer vision have contributed to various fields of study. For instance, while assessing medical images, practitioners can recognize if there is an anomaly. CNNs generally consist of three kinds of layers: convolution layers, pooling layers, and full- connected layers. Convolution layers are responsible for learning features and capturing the Spatial and Temporal dependencies between the features by application of relevant filters. The pooling layer is responsible for reducing the size of feature maps to capture more semantic information than spatial information. Before the fully connected layer, the output of the convolutional and pooling layer is flattened to make a fully connected layer. A loss function is used to calculate the error and the is back propagated to update the values of learnable parameters. In recent years several CNN architectures are developed with various such arrangements: AlexNet, VGGNet, GoogleNet, ResNet, ResNeXt, Squeeze and Excitation Net, DenseNet, and EfficientNet. Convolutional neural networks are used in various applications in the categories of image classification, detection, and segmentation, etc. They are known to be a black box, as the training is according to the task and domain. One major limitation is the unclarity of results i.e., the reason for a particular outcome. One way to tackle this problem head-on is to have such a model that focuses on relevant parts of the image and can be visualized by the doctors. Attention models were proposed. Transformers consist of multiple blocks of identical encoders and decoders, which were composed of self-attention block and feed-forward networks. Transformers consist of an extra attention block, which focuses on the relevant part of the sequence. The performance of the transfer models was state-of-the-art in tasks related to natural language processing. The model was called Vision Transformers (ViT). They divided the entire image into small