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

Coupled-channel analysis of the possible D(∗)D(∗)D^{(*)}D^{(*)}, Bˉ(∗)Bˉ(∗)\bar{B}^{(*)}\bar{B}^{(*)} and D(∗)Bˉ(∗)D^{(*)}\bar{B}^{(*)} molecular states

We perform a coupled-channel study of the possible deuteron-like molecules with two heavy flavor quarks, including the systems of $D^{(*)}D^{(*)}$ with double charm, $\bar{B}^{(*)}\bar{B}^{(*)}$ with double bottom and $D^{(*)}\bar{B}^{(*)}$ with both charm and bottom, within the one-boson-exchange model. In our study, we take into account the S-D mixing which plays an important role in the formation of the loosely bound deuteron, and particularly, the coupled-channel effect in the flavor space. According to our calculation, the states $D^{(*)}D^{(*)}[I(J^P)=0(1^+)]$ and $(D^{(*)}D^{(*)})_s[J^P=1^+]$ with double charm, the states $\bar{B}^{(*)}\bar{B}^{(*)}[I(J^P)=0(1^+),0(2^+),1(0^+),1(1^+),1(2^+)]$, $(\bar{B}^{(*)}\bar{B}^{(*)})_s[J^P=0^+,1^+,2^+]$ and $(\bar{B}^{(*)}\bar{B}^{(*)})_{ss}[J^P=0^+,1^+,2^+]$ with double bottom, and the states $D^{(*)}\bar{B}^{(*)}[I(J^P)=0(0^+),0(1^+)]$ and $(D^{(*)}\bar{B}^{(*)})_s[J^P=0^+,1^+]$ with both charm and bottom are good molecule candidates. However, the existence of the states $D^{(*)}D^{(*)}[I(J^P)=0(2^+)]$ with double charm and $D^{(*)}\bar{B}^{(*)}[I(J^P)=1(1^+)]$ with both charm and bottom is ruled out.Comment: 1 figure added, published in Physical Review

Destination advertising in a smarter way: A machine learning model for DMOs’ photo selection

Photos are important carriers for destination image communication. Currently, effectively and efficiently selecting appropriate photos for destination promotion remains a major challenge for DMOs, and has caused the discrepancy between projected and received images. In the process of photo selection, contents that can best evoke viewers’ potential motives should be highly considered. This project proposes and implements a machine learning based model to assist DMOs with photo content selection. This protocol can rank candidate photos describing a specific theme from viewers’ perspective. In our study, over 190,000 Flickr photos of New York City were analyzed to demonstrate the effectiveness of our approach. The results indicate that the proposed method can facilitate the selection of destination photos and address the well-known gap between the projected image and the received image

Maritime coverage enhancement using UAVs coordinated with hybrid satellite-terrestrial networks

Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications. In practice, UAV-aided aerial base stations are not separate. Instead, they rely on existing satellites/terrestrial systems for spectrum sharing and efficient backhaul. In this case, how to coordinate satellites, UAVs and terrestrial systems is still an open issue. In this paper, we deploy UAVs for coverage enhancement of a hybrid satellite-terrestrial maritime communication network. Using a typical composite channel model including both large-scale and small-scale fading, the UAV trajectory and in-flight transmit power are jointly optimized, subject to constraints on UAV kinematics, tolerable interference, backhaul, and the total energy of the UAV for communications. Different from existing studies, only the location-dependent large-scale channel state information (CSI) is assumed available, because it is difficult to obtain the small-scale CSI before takeoff in practice and the ship positions can be obtained via the dedicated maritime Automatic Identification System. The optimization problem is non-convex. We solve it by using problem decomposition, successive convex optimization and bisection searching tools. Simulation results demonstrate that the UAV fits well with existing satellite and terrestrial systems, using the proposed optimization framework

Optimal capacity reliability design of networks based on genetic algorithm

The cost, capacity and reliability of components vary in different component types, and the optimal component combination is determined by minimizing the total cost under the constraint of network capacity reliability requirement. To solve the problem that the gradient method can only be applied for networks whose capacity and reliability of components monotonically increase with the cost, a general optimization model is presented, and a Genetic Algorithm (GA) method using the minimal path sets to calculate the network capacity reliability is proposed to solve this optimal capacity reliability design problem. The optimal types of both nodes and links can be obtained using our optimization method. Our case study on ARPA network shows that our algorithm is efficient for the problem with good convergence and search performance

Inference for high-dimensional linear expectile regression with de-biased method

In this paper, we address the inference problem in high-dimensional linear expectile regression. We transform the expectile loss into a weighted-least-squares form and apply a de-biased strategy to establish Wald-type tests for multiple constraints within a regularized framework. Simultaneously, we construct an estimator for the pseudo-inverse of the generalized Hessian matrix in high dimension with general amenable regularizers including Lasso and SCAD, and demonstrate its consistency through a new proof technique. We conduct simulation studies and real data applications to demonstrate the efficacy of our proposed test statistic in both homoscedastic and heteroscedastic scenarios.Comment: 34 page