Computer Science: Convolution Neural Networks - Inside the black box -

Computer Science: Convolution Neural Networks – Inside the black box –

for this demonstration we will treat the image as a whole a colored digital image is composed of three primary color channels carrying intensity information in a grayscale format in the analysis of these images the computer sorts these pixels into a matrix of numbers for each pixel the computer assigns a numerical value from 0 to 1 based on a grayscale where black represents an intensity of 0 and white represents full intensity 1 to extract image features filters are used a filter is a matrix of numbers which when applied to the image matrix extracts different features to make this process visible we have scaled up the pixels we generate our own filters as part of the training process these filters have the specific purpose of identifying tumor features a layer of convolved images is generated by running filters across the grid convolutional neural networks consists of many such layers where each image is extracting different features from the previous layer we then use local operations to downscale these images to produce a so-called pooling layer this is done to aggregate the features and reduce dimensionality a second convolved layer is generated by running a different set of filters over the pooled images in this case 16 filters in total the pattern of convolving and pooling continues until we have generated sufficient feature information network configurations can range from just a few to thousands of layers leading to many millions of trainable parameters we consolidate the final layers of feature images which results in values representing the probability of the patient's outcome the difference between the predicted output and the desired output is measured using a mathematical function often called a loss function this calculation provides a point of orientation that guides the necessary adjustments to the filter values we then use an iterative optimization method to minimize this loss function it uses a method called back propagation to compute the values needed to adjust the filter parameters in the network this is done by sending new images through the neural network and adjusting the initial filters until the output image is approximating the ground truth well enough this process is called training

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