Because of their state-of-the-art performance in computer vision, CNNs are becoming increasingly popular in a variety of fields, including medicine. However, as neural networks are black box function approximators, it is difficult, if not impossible, for a medical expert to reason about their output. This could potentially result in the expert distrusting the network when he or she does not agree with its output. In such a case, explaining why the CNN makes a certain decision becomes valuable information. In this paper, we try to open the black box of the CNN by inspecting and visualizing the learned feature maps, in the field of dermatology. We show that, to some extent, CNNs focus on features similar to those used by dermatologists to make a diagnosis. However, more research is required for fully explaining their output.
In @cite_6 , the authors propose a visualization technique to give some insight into the function of the intermediate feature maps of a trained CNN, by attaching a deconvolutional network to each of its convolutional layers. While a CNN maps the input from the image space to a feature space, a deconvolutional network does the opposite (mapping from a feature space back to the image space), by reversing its operations. This is done by a series of unpooling, rectifying and filtering operations. The authors use a deconvolutional network to visualize the features that result in the highest activations in a given feature map. Furthermore, they evaluate the sensitivity of a feature map, to the occlusion of a certain part of the input image, and the effect it has on the class score for the correct class.
@cite_6
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The intermediate map responses of a Convolutional Neural Network (CNN) contain contextual knowledge about its input. In this paper, we present a framework that uses these activation maps from several layers of a CNN as features to a Deep Belief Network (DBN) using transfer learning to provide an understanding of an input image. We create a representation of these features and the training data and use them to extract more information from an image at the pixel level, hence gaining understanding of the whole image. We experimentally demonstrate the usefulness of our framework using a pretrained model and use a DBN to perform segmentation on the BAERI dataset of Synthetic Aperture Radar (SAR) imagery and the CAMVID dataset with a relatively smaller training dataset.