Synthetic data in medical imaging offers numerous benefits, including the ability to augment datasets with diverse and realistic images where real data is limited. This reduces the costs and labor associated with annotating real images. Synthetic data also provides an ethical alternative to using sensitive patient data, which helps with education and training without compromising patient privacy.
]]>Deep neural networks (DNNs) have been successfully applied to volume segmentation and other medical imaging tasks. They are capable of achieving state-of-the-art accuracy and can augment the medical imaging workflow with AI-powered insights. However, training robust AI models for medical imaging analysis is time-consuming and tedious and requires iterative experimentation with parameter…
]]>Deep Learning in medical imaging has shown great potential for disease detection, localization, and classification within radiology. Deep Learning holds the potential to create solutions that can detect conditions that might have been overlooked and can improve the efficiency and effectiveness of the radiology team. However, for this to happen data scientists and radiologists need to collaborate…
]]>