ECSD-Net: A joint optic disc and cup segmentation and glaucoma classification network based on unsupervised domain adaptation.

BACKGROUND AND OBJECTIVES: Glaucoma can cause irreversible vision loss and even blindness, and early diagnosis can help prevent vision loss. Analyzing the optic disc and optic cup helps diagnose glaucoma, which motivates many computer-aided diagnosis methods based on deep learning networks. However, the performance of the trained model on new datasets is seriously hindered due to the distribution gap between different datasets. Therefore, we aim to develop an unsupervised learning method to solve this problem and improve the prediction performance of the model on new datasets. METHODS: In this paper, we propose a novel unsupervised model based on adversarial learning to perform the optic disc and cup segmentation and glaucoma screening tasks in a more generalized and efficient manner. We adopt an efficient segmentation and classification network and employ unsupervised domain adaptation technology on the output space of the segmentation network to solve the domain shift problem. We conduct glaucoma screening task by combining classification and segmentation networks to obtain more stable and efficient glaucoma screening prediction. RESULTS: We verify the effectiveness and efficiency of our proposed method on three public datasets, the REFUGE, DRISHTI-GS and RIM-ONE-r3 datasets. The experimental results demonstrate that the proposed method can effectively alleviate the deterioration of segmentation performance caused by domain shift and improve the accuracy of glaucoma screening. Furthermore, the proposed method outperforms state-of-the-art unsupervised optic disc and cup segmentation domain adaptation methods. CONCLUSIONS: The proposed method can assist clinicians in screening and diagnosis of glaucoma and is suitable for real-world applications.

Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network.

BACKGROUND: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup. METHODS: In this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider network to improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset. RESULTS: The experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7[Formula: see text] in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79[Formula: see text] on the REFUGE dataset, respectively. CONCLUSIONS: The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.

A size-invariant convolutional network with dense connectivity applied to retinal vessel segmentation measured by a unique index.

BACKGROUND AND OBJECTIVES: Retinal vessel segmentation (RVS) helps in diagnosing diseases such as hypertension, cardiovascular diseases, and others. Convolutional neural networks are widely used in RVS tasks. However, how to comprehensively evaluate the segmentation results and how to improve the networks' learning ability are two great challenges. METHODS: In this paper, we proposed an ingenious index: fusion score (FS), which provides an overall measure for those binary images. The FS converts multiple metrics into a single target, and therefore facilitates the optimal threshold's selection and models' comparison. In addition, We simultaneously combined size-invariant feature maps and dense connectivity together to improve the traditional CNN's learning ability. Therefore, a size-invariant convolutional network with dense connectivity is designed for RVS. The size-invariant skill helps the deep layers create feature maps with high resolution. The dense connectivity technique is utilized to integrate those hierarchical features and reuse characteristic maps to enhance the network's learning ability. Finally, an optimized threshold is used on the output image to obtain a binary image. RESULTS: The results of experiments conducted on two shared retinal image databases, DRIVE and STARE, demonstrate that our approach outperforms other techniques when evaluated in terms of F1-score, Matthews correlation coefficient (MCC), G-mean and FS. In addition, the cross training reveals that our method has stronger robustness with respect to training sets. Segmenting a 565 × 584 image only takes 39 ms with a single GPU (graphics processing unit). CONCLUSIONS: Compared with those traditional metrics, the FS is a better indicator to measure the results of RVS tasks. The experimental results revealed that the proposed method is more suitable for real-world applications.

A Joint Power and Channel Scheduling Scheme for Underlay D2D Communications in the Cellular Network.

Device-to-device (D2D) communication, as one of the promising candidates for the fifth generation mobile network, can afford effective service of new mobile applications and business models. In this paper, we study the resource management strategies for D2D communication underlying the cellular networks. To cater for green communications, our design goal is to the maximize ergodic energy efficiency (EE) of all D2D links taking into account the fact that it may be tricky for the base station (BS) to receive all the real-time channel state information (CSI) while guaranteeing the stability and the power requirements for D2D links. We formulate the optimization problem which is difficult to resolve directly because of its non-convex nature. Then a novel maximum weighted ergodic energy efficiency (MWEEE) algorithm is proposed to solve the formulated optimization problem which consists of two sub-problems: the power control (PC) sub-problem which can be solved by employing convex optimization theory for both cellular user equipment (CUE) and D2D user equipment (DUE) and the channel allocation (CA) sub-problem which can be solved by obtaining the weighted allocation matrix. In particular, we shed light into the impact on EE metric of D2D communication by revealing the nonlinear power relationship between CUE and DUE and taking the QoS of CUEs into account. Furthermore, simulation results show that our proposed algorithm is superior to the existing algorithms.

Bulk RKKY signatures of topological phase transition in silicene.

Silicene offers an ideal platform for exploring the phase transition due to strong spin-orbit interaction and its unique structure with strong tunability. With applied electric field and circularly polarized light, silicone is predicted to exhibit rich phases. We propose that these intricate phase transitions can be detected by measuring the bulk Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction. We have in detail analyzed the dependence of RKKY interaction on phase parameters for different impurity configurations along zigzag direction. Importantly, we present an interesting comparison between different terms of RKKY interaction with phase diagram. It is found that the in-plane and out-of-plane terms can exhibit the local extreme value or change of sign at the phase critical point and remarkable difference in magnitude for different phase regions. Consequently, the magnetic measurement provides unambiguous signatures to identify various types of phase transition simultaneously, which can be carried out with present technique.