Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis

Several factors, including advances in computational techniques and algorithms, the availability of graphical processing units, and the assembly of large training datasets, have led to the establishment of DL as the dominant method for computer vision tasks. Competitions in image processing have focused effort on particular tasks and have been useful in revealing which approaches are the most successful. DL algorithms have attained expert level performance in the detection of breast cancer metastases in lymph nodes, and demonstrated superior accuracy compared with previous feature-engineered methods of histology analysis. DL can facilitate large-scale morphology-based research, such as in the recent mapping and analysis of tumor infiltrating lymphocyte patterns in thousands of specimens from the Cancer Genome Atlas digital slide repository. Deep learning refers to a set of computer models that have recently been used to make unprecedented progress in the way computers extract information from images. These algorithms have been applied to tasks in numerous medical specialties, most extensively radiology and pathology, and in some cases have attained performance comparable to human experts. Furthermore, it is possible that deep learning could be used to extract data from medical images that would not be apparent by human analysis and could be used to inform on molecular status, prognosis, or treatment sensitivity. In this review, we outline the current developments and state-of-the-art in applying deep learning for cancer diagnosis, and discuss the challenges in adapting the technology for widespread clinical deployment. Deep learning refers to a set of computer models that have recently been used to make unprecedented progress in the way computers extract information from images. These algorithms have been applied to tasks in numerous medical specialties, most extensively radiology and pathology, and in some cases have attained performance comparable to human experts. Furthermore, it is possible that deep learning could be used to extract data from medical images that would not be apparent by human analysis and could be used to inform on molecular status, prognosis, or treatment sensitivity. In this review, we outline the current developments and state-of-the-art in applying deep learning for cancer diagnosis, and discuss the challenges in adapting the technology for widespread clinical deployment. use of computers to model some or all aspects of human intelligence. Includes DL and other machine learning methods, as well as previous knowledge-based approaches that attempted to hard code inference rules. form of DL that is particularly well suited to image analysis. CNNs use alternating convolutional and pooling operations to extract spatially invariant features from input data, while limiting the number of parameters in the network. form of machine learning that uses complex multilayered architectures to extract progressive degrees of abstraction from input data. machine learning system that maps input data (after preprocessing) directly to predictions, without the use of a separate feature extraction step. form of integrated circuit that has been designed specifically to efficiently alter memory for the display of computer graphics. Their highly parallelized structure is also efficient at the large-scale matrix operations that are used in neural networks. features used for prediction that have been manually selected or inferred by the designer of the model. application of statistical methods to adjust the parameters of a model based on training data, rather than being explicitly programmed. transformations applied to an image prior to using it as input for a CNN, such as normalization, standardization, resizing, cropping, rotation, or color adjustment. field of research that aims to extract minable data from radiographic imaging. machine learning algorithm that uses a large number of individually weak decision trees to generate robust predictions. delineation of different normal and/or abnormal structures or regions. Examples of segmentation tasks include outlining different organs on radiographic images and mapping regions of invasive cancer on histology. machine learning algorithm that uses a set of hyperplanes to distinguish between classes of data with the widest possible margin. use of advanced scanning technology to scan entire glass histology slides at a sufficiently high resolution for pathologic analysis (typically 200–400 X magnification).