10,772 research outputs found
Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemes
Image analysis using more than one modality (i.e. multi-modal) has been
increasingly applied in the field of biomedical imaging. One of the challenges
in performing the multimodal analysis is that there exist multiple schemes for
fusing the information from different modalities, where such schemes are
application-dependent and lack a unified framework to guide their designs. In
this work we firstly propose a conceptual architecture for the image fusion
schemes in supervised biomedical image analysis: fusing at the feature level,
fusing at the classifier level, and fusing at the decision-making level.
Further, motivated by the recent success in applying deep learning for natural
image analysis, we implement the three image fusion schemes above based on the
Convolutional Neural Network (CNN) with varied structures, and combined into a
single framework. The proposed image segmentation framework is capable of
analyzing the multi-modality images using different fusing schemes
simultaneously. The framework is applied to detect the presence of soft tissue
sarcoma from the combination of Magnetic Resonance Imaging (MRI), Computed
Tomography (CT) and Positron Emission Tomography (PET) images. It is found from
the results that while all the fusion schemes outperform the single-modality
schemes, fusing at the feature level can generally achieve the best performance
in terms of both accuracy and computational cost, but also suffers from the
decreased robustness in the presence of large errors in any image modalities.Comment: Zhe Guo and Xiang Li contribute equally to this wor
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
Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.
We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects
Deep Affordance-grounded Sensorimotor Object Recognition
It is well-established by cognitive neuroscience that human perception of
objects constitutes a complex process, where object appearance information is
combined with evidence about the so-called object "affordances", namely the
types of actions that humans typically perform when interacting with them. This
fact has recently motivated the "sensorimotor" approach to the challenging task
of automatic object recognition, where both information sources are fused to
improve robustness. In this work, the aforementioned paradigm is adopted,
surpassing current limitations of sensorimotor object recognition research.
Specifically, the deep learning paradigm is introduced to the problem for the
first time, developing a number of novel neuro-biologically and
neuro-physiologically inspired architectures that utilize state-of-the-art
neural networks for fusing the available information sources in multiple ways.
The proposed methods are evaluated using a large RGB-D corpus, which is
specifically collected for the task of sensorimotor object recognition and is
made publicly available. Experimental results demonstrate the utility of
affordance information to object recognition, achieving an up to 29% relative
error reduction by its inclusion.Comment: 9 pages, 7 figures, dataset link included, accepted to CVPR 201
Sentient Networks
In this paper we consider the question whether a distributed network of
sensors and data processors can form "perceptions" based on the sensory data.
Because sensory data can have exponentially many explanations, the use of a
central data processor to analyze the outputs from a large ensemble of sensors
will in general introduce unacceptable latencies for responding to dangerous
situations. A better idea is to use a distributed "Helmholtz machine"
architecture in which the collective state of the network as a whole provides
an explanation for the sensory data.Comment: PostScript, 14 page
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