A 3D-CAE-CNN model for Deep Representation Learning of 3D images

Abstract

Deep Representation Learning technologies based on supervised Convolutional Neural Networks (CNNs) have attained significant interest mainly due to their superior performance for learning abstract and robust features used in object detection and image classification tasks. However, to efficiently train such models requires a large number of labeled instances especially when these instances are high dimensional such as for 3-Dimensional (3D) Image inputs. Due to this extra dimension the dimensionality of such instances increases drastically. Therefore, the utilization of Unsupervised CNNs topologies such 3D Convolutional AutoEncoders (3D-CAE) have also been proposed. CAEs can learn features (and later used for classification tasks using common machine learning classifiers), without relying on instance labels and thus they are not prone to label limitation. Nevertheless, it is not clear if the features that CAEs learn, are relevant regarding the classification or object detection task since these features are learned via no target output class. For these reasons, in this work we combine 3D-CAE and 3D-CNN to work synergistically together in order to build a hybrid deep representation learning framework model which exploits the advantages of both unsupervised and supervised representation/feature learning approaches, applied on 3D Image inputs. In order to evaluate our strategy, we performed extensive experimental simulations for the DeepFake and Pneumonia detection problems utilizing Video and 3D Scans datasets respectively. Our proposed framework outperformed all the other utilized frameworks, revealing the efficiency of our applied methodology.