Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

Neonates do not have the ability to either articulate pain or communicate it non-verbally by pointing. The current clinical standard for assessing neonatal pain is intermittent and highly subjective. This discontinuity and subjectivity can lead to inconsistent assessment, and therefore, inadequate treatment. In this paper, we propose a multi-channel deep learning framework for assessing neonatal pain from videos. The proposed framework integrates information from two pain indicators or channels, namely facial expression and body movement, using convolutional neural network (CNN). It also integrates temporal information using a recurrent neural network (LSTM). The experimental results prove the efficiency and superiority of the proposed temporal and multi-channel framework as compared to existing similar methods.Comment: Accepted to IEEE SMC 201

Multiscale combination of physically-based registration and deformation modeling

Abstract 1 In this paper we present a novel multiscale approach to recovery of nonrigid motion from sequences of registered intensity and range images. The main idea o f our approach is that a nite element (FEM) model can naturally handle both registration and deformation modeling using a single model-driving strategy. The method includes a multiscale iterative algorithm based on analysis of the undirected Hausdor distance to recover corresp ondences. The method is evaluated with resp ect to speed, accur acy, and noise sensitivity. A dvantages of the pr oposed a p p r oach ar e demonstr ated using man-made elastic materials and human skin motion. Experiments with regular grid featur esare used for performance comparison with a conventional approach (separate snakes and FEM models). It is shown that the new method does not requir ea grid and can adapt the model to available object featur es