Object detection is an important branch of panoramic image scene understanding. Panoramic images possess characteristics such as a wide field of view, significant distortion, and rich content, which leads to constant ...
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Semantic segmentation of unmanned aerial vehicle (UAV) remote sensing images has been widely used in various fields of remote sensing;however, manual visual interpretation methods are inefficient and highly dependent ...
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The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain *** noninvasively evaluate GG,an automatic prediction method is proposed base...
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The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain *** noninvasively evaluate GG,an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum ***,a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion ***,the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion ***,in the network training,a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed,which further improves the prediction performance of the ***-perimental results show that the proposed method is better than the traditional network model in predicting GG *** quadratic weighted Kappa is 0.4711 and the positive predictive value for predicting clinically significant cancer is 0.9369.
Fluorescence in situ hybridization (FISH) is a molecular cytogenetic technique that provides reliable imaging biomarkers for the diagnosis of cancer and genetic diseases at the cellular level. An important prerequisit...
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Virtual reality (VR) applications have revolutionized digital interaction by providing immersive experiences.360° VR video streaming has experienced significant growth and popularity as a pivotal VR application. ...
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Virtual reality (VR) applications have revolutionized digital interaction by providing immersive experiences.360° VR video streaming has experienced significant growth and popularity as a pivotal VR application. However, the combination of limited network bandwidth and the demand for high-quality videos frequently hinders the achievement of a satisfactory quality of experience (QoE). Although prior methods have enhanced QoE, the effects of decoding latency have been poorly studied. It is technically challenging to design a quality adaptation algorithm that can balance the pursuit of high-quality videos and the limitation of limited bandwidth resources. To address this challenge, we propose an edge-end architecture for 360° VR video streaming and aim to enhance overall QoE by solving a performance optimization problem. Specifically, our experiments on commercial mobile devices in real-world situations reveal that decoding latency significantly influences QoE. First, decoding latency plays a major role in contributing to end-to-end latency, which exceeds the transmission latency. Second, decoding latency can differ considerably between devices with varying computational capabilities. Building on this insight, we propose a novel latency-aware quality adaptation (LAQA) algorithm. LAQA lies in developing a solution that can allocate video quality in real-time and enhance overall QoE. LAQA involves not only the quality of the received content, the transmission latency and the quality variance, but also the decoding latency and the fairness of the user quality. Subsequently, we formulate a combinatorial optimization problem to maximize overall QoE. Through extensive validation with experimental data from real-world situations, LAQA offers a promising approach to enhance QoE and ensure fairness performance in different devices. In particular, LAQA achieves 16.77% and 10.66% enhancement over the state-of-the-art combinatorial optimization and reinforcement learning algorithm
Tissue segmentation in histopathological images plays a crucial role in computational pathology, owing to its significant potential to indicate the prognosis of cancer patients. Presently, numerous Weakly Supervised S...
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Gradient compression is a promising approach to alleviating the communication bottleneck in data parallel deep neural network (DNN) training by significantly reducing the data volume of gradients for synchronization. ...
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Gradient compression is a promising approach to alleviating the communication bottleneck in data parallel deep neural network (DNN) training by significantly reducing the data volume of gradients for synchronization. While gradient compression is being actively adopted by the industry (e.g., Facebook and AWS), our study reveals that there are two critical but often overlooked challenges: 1) inefficient coordination between compression and communication during gradient synchronization incurs substantial overheads, and 2) developing, optimizing, and integrating gradient compression algorithms into DNN systems imposes heavy burdens on DNN practitioners, and ad-hoc compression implementations often yield surprisingly poor system performance. In this paper, we propose a compression-aware gradient synchronization architecture, CaSync, which relies on flexible composition of basic computing and communication primitives. It is general and compatible with any gradient compression algorithms and gradient synchronization strategies and enables high-performance computation-communication pipelining. We further introduce a gradient compression toolkit, CompLL, to enable efficient development and automated integration of on-GPU compression algorithms into DNN systems with little programming burden. Lastly, we build a compression-aware DNN training framework HiPress with CaSync and CompLL. HiPress is open-sourced and runs on mainstream DNN systems such as MXNet, TensorFlow, and PyTorch. Evaluation via a 16-node cluster with 128 NVIDIA V100 GPUs and a 100 Gbps network shows that HiPress improves the training speed over current compression-enabled systems (e.g., BytePS-onebit, Ring-DGC and PyTorch-PowerSGD) by 9.8%-69.5% across six popular DNN models. IEEE
With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. ...
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With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.
Ethiopia, known as the birthplace of coffee, relies on coffee exports as a major source of foreign currency. This research paper focuses on developing a hybrid feature mining technique to automatically classify Ethiop...
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Background: In this research, a novel algorithm is formulated through the combination of gradient and adaptive thresholding. A set of 5 X 5 convolution kernels were generated to determine the gradients in the four mai...
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