"A breakthrough in the field of deep learning was in 1998, when Lecun and colleagues applied their novel convolutional neural network, LeNet, to handwritten digit classification 4. However, deep learning methods did not receive wide attention until 2012. That year, at the ImageNet challenge, Krizhevsky and Hinton5 developed a CNN named AlexNet that surpassed all the other competing classic machine learning techniques and won the competition. Today, deep learning and CNNs are considered to represent the state of the art in image analysis 2,3."
Expected headings
"Deep learning in radiology"
"Deep learning is a subset of machine learning based on multi-layered (a.k.a. “deep“) artificial neural networks. Their highly flexible architectures can learn directly from data (such as images, video or text) without the need of hand-coded rules and can increase their predictive accuracy when provided with more data."
"A breakthrough in the field of deep learning was in 1998, when Lecun and colleagues applied their novel convolutional neural network, LeNet, to handwritten digit classification 4. However, deep learning methods did not receive wide attention until 2012. That year, at the ImageNet challenge, Krizhevsky and Hinton5 developed a CNN named AlexNet that surpassed all the other competing classic machine learning techniques and won the competition. Today, deep learning and CNNs are considered to represent the state of the art in image analysis 2,3."