Computer Vision News - May 2022

41 Vision Transformers in Medical Computer Vision Medical Computer Vision . A survey is performed on the application of Vision transformers in different areas of medical computer vision such as image-based disease classification, anatomical structure segmentation, registration, region-based lesion detection, reconstruction using multiple medical imaging modalities that greatly assist in medical diagnosis and hence treatment process, and in the following figure from the paper you can see how different reviews have approached the subject. Medical images contain an ample information that is the key for medical diagnosis and hence treatment. The healthcare data comprises 90% of imaging data, so considered as the primary source for medical intervention and analysis. Multiple medical imaging modalities such as Computed Tomography (CT), ultrasound, X-ray radiography, MR Imaging (MRI), and pathology are commonly used for medical imaging diagnostics. The analysis of these images by analysts is limited by human subjectivity, time constraints, and variation of interpretation. Several challenging factors associated with medical imaging modalities such as expensive data acquisition, dense pixel resolution, lack of standard image acquisition. Those techniques in terms of tool and scanning settings, modality-specific artefacts and hugely imbalanced data in negative and positive classes are major hindrance in translating AI based diagnosis into clinical practice. Convolutional Neural Networks (CNN) are a type of deep learning architecture. CNNs are potentially the most popular deep learning architecture for its distinguished capabilities to exploit the spatial and temporal relationship between the features of images. CNNs have achieved notable accomplishment in medical imaging applications, such as, determining the presence and then identifying the type of malignancy (Classification), locating the patient's lesion (Detection), extracting the desired object (organ) from a medical image (Segmentation), placing separate images in a common frame of reference for comparing or integrating the information they contain (Registration), synthesizing images for balancing dataset (Generative Modeling). CNNs are very good at feature extraction tasks. CNNs lose the global context of the features. Increasing the number of filters improves the representation capacity but at the cost of computation . Various architectural changes are suggested by researchers for an efficient solution over time and leading to attention mechanisms. Using attention mechanism, regions of the image are captured, to which a CNN should pay attention, and forwarded to deeper layers. Researchers have demonstrated that replacing convolutional layer with attention has improved performance. It gets the best out of the attention mechanism to incorporate global context in the image features without compromising computational efficiency . The potential of the vision transformers is further explored by many researchers for solving various problems. In this work it is highlighted the contribution of vision transformers to circumvent the challenges in automatic diagnostic of diseases using medical imaging modalities and their applications in medical computer vision tasks.