Deformable Image Registration with Progressively Trained Convolutional Neural Networks PROJECT TITLE : Progressively Trained Convolutional Neural Networks for Deformable Image Registration ABSTRACT: The quick registration periods of Deep Learning-based algorithms for deformable picture registration make them viable alternatives to conventional methods. For complicated deformation fields, a multi-resolution technique is necessary to estimate bigger displacements. The solution proposed in this article is to train neural networks incrementally. A convolutional neural network is first trained on low-resolution pictures and deformation fields before being trained on a larger version of the network. During training, additional layers are added to the network that are trained on more detailed data. For example, we show that a network trained this manner can learn greater displacements without reducing registration precision. This results in a more robust system that is less sensitive to massive misregistrations. On the intra-patient lung CT registration problem, we produce huge numbers of ground truth examples using random synthetic alterations applied to a training set of pictures. To determine how the progressive learning technique affects training, we examine the learnt representations in the gradually increasing network. A gradual training process leads to better registration accuracy while learning complicated deformations, as demonstrated in this study Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Object Transfer Detection in Progress Deep Generative Image Quality Prediction