An Overview of Deep Learning in Medical Imaging Focusing on MRI images
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Machine learning has seen some dramatic developments recently, leading to a lot of interest from industry, academia and popular culture. These are driven by breakthroughs in artificial neural networks, often termed deep learning, a set of techniques and algorithms that enable computers to discover complicated patterns in large data sets. Feeding the breakthroughs is the increased access to data ("big data"), user-friendly software frameworks, and an explosion of the available compute power, enabling the use of neural networks that are deeper than ever before. These models nowadays form the sta...
Machine learning has seen some dramatic developments recently, leading to a lot of interest from industry, academia and popular culture. These are driven by breakthroughs in artificial neural networks, often termed deep learning, a set of techniques and algorithms that enable computers to discover complicated patterns in large data sets. Feeding the breakthroughs is the increased access to data ("big data"), user-friendly software frameworks, and an explosion of the available compute power, enabling the use of neural networks that are deeper than ever before. These models nowadays form the state-of-the-art approach to a wide variety of problems in computer vision, language modeling and robotics. Deep learning rose to its prominent position in computer vision when neural networks started outperforming other methods on several high-profile image analysis benchmarks. Most famously on the Image Net Large-Scale Visual Recognition Challenge (ILSVRC) in 2012 when a deep learning model (a convolutional neural network) halved the second-best error rate on the image classification task.
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