中科院计算所-计算机技术研究所计算技术研究所发表的相关论文
Di Huang, Xishan Zhang, Rui Zhang, He Deyuan, Jiaming Guo, Chang Liu, Tian Zhi,Qi Guo, Du Zidong,Shaoli Liu, Tianshi Chen,Yunji Chen
national conference on artificial intelligence, (2020)
Cited by22BibtexViews53DOI
0
0
Jiuwen Zhu, Yuexiang Li, Yifan Hu, Kai Ma, S Kevin Zhou,Yefeng Zheng
Medical Image Anal., (2020): 101746
We proposed a novel self-supervised learning pretext task, involving three transformations— cube ordering, rotating and masking, to pre-train 3D neural networks for volumetric medical images
Cited by0BibtexViews49DOI
0
0
Dawen Xu, Cheng Chu, Cheng Liu,Ying Wang, Xianzhong Zhou,Lei Zhang,Huaguo Liang,Huawei Li
GLSVLSI '20: Great Lakes Symposium on VLSI 2020 Virtual Event China September,..., pp.457-462, (2020)
Processing-in-Memory (PIM) or Near-Data Processing has been recognized as the most potential solution to resolve the ever-aggravating memory wall especially as the thrive of memory-intensive scale-out workloads such as graph computing and data analytics. However, when the future ...
Cited by0BibtexViews38DOI
0
0
Han Li,Hu Han, S. Kevin Zhou
medical image computing and computer assisted intervention, pp.417-428, (2020)
(ULD) in computed tomography plays an essential role in computer-aided diagnosis systems. Many detection approaches achieve excellent results for ULD using possible bounding boxes (or anchors) as proposals. However, empirical evidence shows that using anchor-based proposals leads...
Cited by0BibtexViews26DOI
0
0
Qingsong Yao, Zecheng He, Hu Han, S. Kevin Zhou
medical image computing and computer-assisted intervention, pp.692-702, (2020)
Recent methods in multiple landmark detection based on deep convolutional neural networks (CNNs) reach high accuracy and improve traditional clinical workflow. However, the vulnerability of CNNs to adversarial-example attacks can be easily exploited to break classification and se...
Cited by0BibtexViews15DOI
0
0
Dawen Xu, Kexin Chu, Cheng Liu,Ying Wang,Lei Zhang,Huawei Li
DATE, pp.963-966, (2020)
Carbon Nanotubu field-effect transistor (CNFET) that promises both higher clock speed and energy efficiency becomes an attractive alternative to the conventional power-hungry CMOS cache. We observe that the CNFET-based cache constructed with typical SRAM cells has distinct energy...
Cited by0BibtexViews26DOI
0
0
Zengming Shen, Yifan Chen, Kevin S. Zhou,Bogdan Georgescu, Xuqi Liu,Thomas S. Huang
ISBI, pp.1765-1769, (2020)
We have presented an approach for learning a CNN for bidirectional Magnetic Resonance Imaging image synthesis
Cited by0BibtexViews75DOI
0
0
Xishan Zhang,Shaoli Liu, Rui Zhang, Chang Liu, Di Huang, Shiyi Zhou, Jiaming Guo,Qi Guo,Zidong Du, Tian Zhi,Yunji Chen
CVPR, pp.2327-2335, (2020)
11Xeon Gold 6154 can only support multiplication between equal bitwidth fixed-point numbers, so in this experiment int16 × int8 is implemented as int16 × int16
Cited by0BibtexViews113DOI
0
0
Zengming Shen, Yifan Chen,Thomas S. Huang, S. Kevin Zhou,Bogdan Georgescu, Xuqi Liu
WACV, pp.1159-1168, (2020)
We propose a self-inverse network learning approach for unpaired image-to-image translation
Cited by0BibtexViews95DOI
0
0
Chen Weiwei,Wang Ying, Yang Shuang, Liu Chen, Zhang Lei
DATE, pp.1283-1286, (2020)
DNN/Accelerator co-design has shown great potential in improving QoR and performance. Typical approaches separate the design flow into two-stage: (1) designing an application-specific DNN model with high accuracy; (2) building an accelerator considering the DNN specific charact...
Cited by0BibtexViews14DOI
0
0
Han Li,Hu Han, Zeju Li, Lei Wang, Zhe Wu, Jingjing Lu, S Kevin Zhou
IEEE Transactions on Medical Imaging, no. 10 (2020): 3053-3063
We conduct experiments and clinical evaluations based on two benchmarking Chest X-rays databases and demonstrate that the performances of real clinical diagnosis and automatic lung disease classification are boosted with the aid of the bone suppressed image
Cited by0BibtexViews37DOI
0
0
The journal of physical chemistry. A, (2020)
When using atom-centered integration grids, the portion of the grid that belongs to a certain atom also moves when this atom is displaced. In the paper, we investigate the moving-grid effect in the calculation of the harmonic vibrational frequencies when using all-electron full-p...
Cited by0BibtexViews14DOI
0
0
CVPR, pp.4272-4281, (2020)
We present a dual domain recurrent network for fast MRI reconstruction with T1 prior embedded
Cited by0BibtexViews48DOI
0
0
CVPR, pp.7747-7756, (2020)
We introduce a Spatially Aware Interpolation NeTwork for medical slice synthesis to alleviate the memory constraint that volumetric data poses
Cited by0BibtexViews91DOI
0
0
IEEE transactions on medical imaging, no. 3 (2019): 634-643
Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training. However, as synthesized data may not accurately simulate the underlying physical mechanisms of CT ...
Cited by10BibtexViews72DOI
0
0
Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp.347-362, (2019)
We present Panthera, the first memory management technique for managed Big Data processing over hybrid memories
Cited by5BibtexViews60DOI
0
0
J. Comput. Sci. Technol., no. 2 (2019): 339-371
The significant gap between the unique feature of graph processing and the hardware features of general-purpose architectures limits the further improvement of performance and energy efficiency
Cited by5BibtexViews75DOI
0
0
Kun Li, Honghui Shang,Yunquan Zhang,Shigang Li, Baodong Wu, Dong Wang, Libo Zhang, Fang Li, Dexun Chen, Zhiqiang Wei
Proceedings of the International Conference for High Performance Computing, Networking, Storage and ..., pp.68, (2019)
With more attention attached to nuclear energy, the formation mechanism of the solute clusters precipitation within complex alloys becomes intriguing research in the embrittlement of nuclear reactor pressure vessel (RPV) steels. Such phenomenon can be simulated with atomic kineti...
Cited by1BibtexViews27DOI
0
0
Jun Zhang,Guangxing Zhang,Qinghua Wu, Binbin Liao,Gaogang Xie
IEEE Transactions on Network and Service Management, no. 1 (2019): 321-333
After regression we evaluate the model by mean absolute error, mean square error and R-squared
Cited by1BibtexViews34DOI
0
0
IEEE/ACM Transactions on Networking, no. 1 (2019): 272-287
It is noteworthy that while smart-retransmission time out seems more aggressive than ER and Tail Loss Probe, the retransmission rate is still kept small as we show through experiments
Cited by1BibtexViews72DOI
0
0
No data, please see others