1,210 research outputs found
Decomposing 3D Neuroimaging into 2+1D Processing for Schizophrenia Recognition
Deep learning has been successfully applied to recognizing both natural
images and medical images. However, there remains a gap in recognizing 3D
neuroimaging data, especially for psychiatric diseases such as schizophrenia
and depression that have no visible alteration in specific slices. In this
study, we propose to process the 3D data by a 2+1D framework so that we can
exploit the powerful deep 2D Convolutional Neural Network (CNN) networks
pre-trained on the huge ImageNet dataset for 3D neuroimaging recognition.
Specifically, 3D volumes of Magnetic Resonance Imaging (MRI) metrics (grey
matter, white matter, and cerebrospinal fluid) are decomposed to 2D slices
according to neighboring voxel positions and inputted to 2D CNN models
pre-trained on the ImageNet to extract feature maps from three views (axial,
coronal, and sagittal). Global pooling is applied to remove redundant
information as the activation patterns are sparsely distributed over feature
maps. Channel-wise and slice-wise convolutions are proposed to aggregate the
contextual information in the third view dimension unprocessed by the 2D CNN
model. Multi-metric and multi-view information are fused for final prediction.
Our approach outperforms handcrafted feature-based machine learning, deep
feature approach with a support vector machine (SVM) classifier and 3D CNN
models trained from scratch with better cross-validation results on publicly
available Northwestern University Schizophrenia Dataset and the results are
replicated on another independent dataset
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
By promising more accurate diagnostics and individual treatment
recommendations, deep neural networks and in particular convolutional neural
networks have advanced to a powerful tool in medical imaging. Here, we first
give an introduction into methodological key concepts and resulting
methodological promises including representation and transfer learning, as well
as modelling domain-specific priors. After reviewing recent applications within
neuroimaging-based psychiatric research, such as the diagnosis of psychiatric
diseases, delineation of disease subtypes, normative modeling, and the
development of neuroimaging biomarkers, we discuss current challenges. This
includes for example the difficulty of training models on small, heterogeneous
and biased data sets, the lack of validity of clinical labels, algorithmic
bias, and the influence of confounding variables
Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring
How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal
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