Deep Learning in Medical Imaging: General Overview

Abstract

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

Keywords: Artificial intelligence; Computer-aided; Convolutional neural network; Machine learning; Precision medicine; Radiology; Recurrent Neural Network.

Fig. 1. Categories of machine learning, including classification, regression, clustering, and dimensionality reduction.

Adapted from http://scikit-learn.org/stable/tutorial/machine_learning_map/(101). GMM = Gaussian mixture model, LLE = locally-linear embedding, PCA = principal component analysis, SGD = stochastic gradient descent, SVC = support vector classification, SVR = support vector regression, VBGMM = variational Bayesian Gaussian mixture model

Fig. 2. Conceptual analogy between real neurons (A) and artificial neurons (B).

Fig. 3. Comparison between shallow learning and deep learning in neural network.

A. Typical deep learning neural network with 3 deep layers between input and output layers. B. Typical artificial neural network with 1 layer between input and output layers.

Fig. 4. Two breakthrough algorithms in deep learning, including unsupervised pre-training and dropout.

Fig. 5. Architecture of convolutional neural networks, including input, Conv., and FC layers.

Conv. = convolutional, FC = fully connected

Fig. 6. Illustration of convolution and pooling methods.

A. Convolution method. B. Max and average pooling methods.

Fig. 7. Example of semantic segmentation in knee MR image.

A. Input MR knee image. B. Feature response maps on layers with different depth in fCNN. C. Output result from fCNN. fCNN = fully convolutional neural network

Fig. 8. Preliminary results of lesion detection on chest radiographs, by using faster R-CNN architecture.

Each result set is composed of 3 rows. First row shows faster R-CNN results, and ground truth lesion mask is delineated by radiologists in second row. Automatic description is provided in third row. A. Faster R-SNN architecture. B. Proposed regions of interest. C. Multiple lesion detection results. R-CNN = regional-convolutional neural network

Fig. 9. Precision medicine based on medical big data, including internet of things, genetics and genomics, medicinal imaging, and mobile monitoring.