Deep learning has shown great success in various machine learning tasks. In image classification tasks, the ability of deep convolutional neural networks (CNNs) to deal with complex image data has proved to be unrivalled. Deep CNNs, however, require large amounts of labeled training data to reach their full potential. In specialised domains such as healthcare, labeled data can be difficult and expensive to obtain. One way to alleviate this problem is to rely on active learning, a learning technique that aims to reduce the amount of labelled data needed for a specific task while still delivering satisfactory performance. We designed a new method that exhibits significantly improved performance over the state-of-the-art Bayesian method in active learning for various image classification tasks.
The image classification task is particularly relevant to the medical industry. IBM researchers estimate that at least 90 percent of all medical data comes in the form of medical images , making it the largest data source in the healthcare industry. According to a recent study by McKinsey , the potential value of deep learning in the medical domain would be enormous, mainly due to machine learning’s enormous potential to enhance diagnostic accuracy. AI solutions are already prevalent in the medical imaging industry, with applications ranging from detection of anatomical and cellular structures, to tissue segmentation, radiology, and disease diagnosis and prognosis. However, most of these techniques rely on rich labeled data sets incorporating image and video inputs, including from MRIs. Our work demonstrated the practical application of active learning, with a focus on the medical domain. Specifically, we developed a new active learning method and used it to successfully detect signs of diabetic retinopathy in images, an eye disease associated with long-standing diabetes.
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