Computer Aided Diagnosis typically uses a clinician expert in their field, such as a breast Radiologists and using a narrow training sample, an algorithm is developed to detect a specific disease pathology.
The advent of deep learning has revolutionized the field of computer vision and machine perception. Deep learning technology combines artificial neural network architectures with massive computing power to perform training and inferences.
On a high level, deep learning involves a two stage process: First a neural network is trained by tuning its numeric weights based on experience. This makes neural nets adaptive to inputs and capable of learning. Second, the network is deployed to run inferences i.e. using its previously trained parameters to classify, recognise, and generally process unknown but similar inputs.
The massively parallel processing capabilities of modern GPU’s make them the ultimate tool in training deep neural networks compared to more traditional CPU-based platforms.
The advent of GPU heralds a new era in computing. It is only now that the sheer volumes of examinations in radiology can be diagnosed using deep learning neural network architectures developed by behold.ai.