39 Data Availability and Quantity in Medical AI Projects (part 2) Computer Vision News data collected with different modalities. One practical application of this technique is to generate synthetic X-ray images from CT images (DRRs), which significantly enlarges the smaller dataset. This technique is different from data augmentation, which adds noise and other changes to existing real data to make it different in a clinically coherent way. Synthetic data generation is done using Generative Adversarial Networks (GANs) or other simulation tools and can produce completely generated data. Artifacts and effects generation is a technique that is similar to data augmentation and is used to address the problem of variety in data. Sometimes, the data contains artifacts that are due to the type of device used rather than the true condition of the patient. For instance, CT images scanned in regions that include metals can produce significant artifacts that can obscure important areas or even alter key clinical values. Patient movement is a common cause of artifacts in medical images. Ultrasound images have specific artifacts like reverberation, shadowing, mirroring, anisotropy, and more. RSIP Vision employs a technique called the support task to improve the accuracy of its models. This involves adding a secondary task, such as segmenting a nearby structure, to support the main task of segmenting an organ or disease. By doing so, the network is less likely to become confused and produce inaccurate results. This technique is particularly useful when dealing with limited data, as it improves the network's accuracy in identifying the region of interest. For example, when segmenting a specific bone using CT, it is helpful to segment all the surrounding bones as well, so that the network can distinguish between them accurately. This technique is also useful for more complex tasks such as tumor segmentation, where the network can learn to differentiate the tumor from surrounding tissues. Additionally, this technique can be used to add contrast to images with less defined contours, such as when segmenting a bone in an MRI scan or analyzing a surgical video. These are just some of the options that can greatly enhance the accuracy, generalization, and robustness of AI modules when working with limited datasets. Contact RSIP Vision to ensure your project is executed in a highly professional manner.
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