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

THE MODERATE ROLE OF PERCEIVED SURVEILLANCE FOR VALUE PERCEPTION IN SOLOMO SERVICES CONTINUANCE

The full-fledged Social-Local-Mobile (SoLoMo) services appear recently as the form of app for Android or iOS system which include Facebook, Instagram, LINE, Google maps, etc. However, no study has attempted to understand the continuance intention among SoLoMo services. Besides, SoLoMo services have provided more powerful means of surveillance to track and profile their users, which might arouse negative feeling. In this study, we apply the consumption value theory to explore the value drivers and investigate the moderating effect of users’ perceived surveillance. The results indicate that social value, emotional value, and functional value are significant drivers for continuance intention. Perceived surveillance moderates the relationship of social value and functional value on continuance intention