Article
Computer Science, Artificial Intelligence
Shaofan Wang, Yukun Liu, Yanfeng Sun, Baocai Yin
Summary: This article proposes a new method called Shuffling Atrous Convolutional U-Net (SACNet) to address two issues in medical image segmentation: disrupted distribution of essential feature of objects and blurred object boundaries. SACNet solves these problems by using the Shuffling Atrous Convolution (SAC) module to merge different atrous convolutional layers, and outperforms other methods in three medical image segmentation tasks.
IET IMAGE PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Zijjian Wang, Yuxuan Huang, Yaqin Zhu, Binxing Xu, Long Chen
Summary: This paper proposes a new spiking neuron model and connection method to improve the performance of SNN in supervised learning, and compares it with other methods. The experimental results demonstrate that our proposed method outperforms previous methods in the same network structure.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zijian Wang, Yanting Zhang, Haibo Shi, Lei Cao, Cairong Yan, Guangwei Xu
Summary: This study introduces a new LIF neuron model with recurrent connections and dynamic presynaptic currents, along with a BP training method for these neurons. Experimental results demonstrate that the model performs exceptionally well on image and text datasets, surpassing previous SNN methods.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Review
Computer Science, Artificial Intelligence
Qiulang Ji, Jihong Wang, Caifu Ding, Yuhang Wang, Wen Zhou, Zijie Liu, Chen Yang
Summary: This paper proposes a Dual-path Multi-scale Attention Guided network (DMAGNet) for medical image segmentation. By introducing Dual-path Multi-scale Attention Fusion Module (DMAF) and Multi-scale Normalized Channel Attention Module (MNCA), accurate segmentation of pathological regions is achieved. Experimental results demonstrate that DMAGNet outperforms the original U-Net method and other advanced methods in brain, lung, and liver segmentation tasks.
IET IMAGE PROCESSING
(2023)
Article
Physics, Multidisciplinary
Yidong Liao, Daniel Ebler, Feiyang Liu, Oscar Dahlsten
Summary: The importance of neural network performance for tasks lies in the initial calibration and training of parameters, existing training methods have issues, proposing the use of quantum superposition of weight configurations can lead to high probability convergence towards the globally optimal solution.
NEW JOURNAL OF PHYSICS
(2021)
Article
Computer Science, Theory & Methods
Peng Qu, Hui Lin, Meng Pang, Xiaofei Liu, Weimin Zheng, Youhui Zhang
Summary: This paper proposes an efficient SNN simulation framework, ENLARGE, that utilizes GPU clusters. The framework has a multi-level architecture, efficient communication methods, and optimization techniques to handle SNN characteristics and improve performance and scalability.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Automation & Control Systems
James B. Aimone
Summary: Neuromorphic computing is an important future technology for the computing industry, but facing challenges in establishing a cohesive research community and realizing the full potential of brain-inspired computing. This requires advancement in device hardware, computing architectures, and algorithms simultaneously, which is unprecedented in the field of computing.
ADVANCED INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Juan Wang, Zetao Zhang, Minghu Wu, Yonggang Ye, Sheng Wang, Ye Cao, Hao Yang
Summary: In this work, an improved BlendMask nuclei instance segmentation framework is proposed, which incorporates dilated convolution aggregation module and context information aggregation module to enhance the performance of detecting and segmenting dense small objects and adhering nuclei. A distributional ranking loss function is also introduced to alleviate the imbalance between the target and the background. The proposed method outperforms several recent classic open-source nuclei instance segmentation methods on the DSB2018 dataset, achieving a 3.6% improvement on AP segmentation metric compared to BlendMask.
IET IMAGE PROCESSING
(2023)
Article
Clinical Neurology
Hans Holthausen, Roland Coras, Yingying Tang, Lily Bai, Irene Wang, Tom Pieper, Manfred Kudernatsch, Till Hartlieb, Martin Staudt, Peter Winkler, Wiebke Hofer, Samir Jabari, Katja Kobow, Ingmar Blumcke
Summary: This study described the clinical phenotype of 19 children with FCD1A, including drug-resistant epilepsy, cognitive impairment, and abnormal EEG findings. Individualized multilobar resections were performed with 47% achieving seizure freedom.DNA methylation analysis distinguished FCD1A samples from all other epilepsy specimens and controls.
Review
Clinical Neurology
Gavin J. B. Elias, Aaron Loh, Dave Gwun, Aditya Pancholi, Alexandre Boutet, Clemens Neudorfer, Juergen Germann, Andrew Namasivayam, Robert Gramer, Michelle Paff, Andres M. Lozano
Summary: Deep brain stimulation (DBS) of various brainstem targets has shown promising results for treating movement disorders, neuropathic pain, and neuropsychiatric conditions. Further large, controlled trials are necessary to fully establish the therapeutic potential of DBS in this complex area.
Article
Computer Science, Artificial Intelligence
Teh-Lu Liao, Chiau-Yuan Peng, Yi-You Hou
Summary: This paper introduces the technology of image processing and artificial intelligence based on cloud computing. A secure multi-party computation (SMPC) encryption method is used to protect important information, and the Berlekamp-Welch (BW) algorithm is used for error correction. The authors also discuss the application of homomorphism in encryption and decryption, and implement a system for image enhancement and convolutional neural networks (CNN) using cloud computing.
IET IMAGE PROCESSING
(2023)
Article
Chemistry, Multidisciplinary
Martin Magdin, Juraj Benc, Stefan Koprda, Zoltan Balogh, Daniel Tucek
Summary: This paper compares the success rates of three different models of multilayer neural networks in the classification phase, using the EmguCV, ML.NET, and Tensorflow.Net libraries. The paper emphasizes the importance of choosing the right model that achieves the required accuracy with minimum training time and introduces an application that allows customization of parameters and integration into other applications.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Man Yao, Hengyu Zhang, Guangshe Zhao, Xiyu Zhang, Dingheng Wang, Gang Cao, Guoqi Li
Summary: Event-based visual paradigm with bio-inspired dynamic perception and μs level temporal resolution has gained research interest. This paper proposes a feature Refine-and-Mask SNN (RM-SNN) that can self-adaptively regulate the spiking response and optimize the membrane potential of spiking neurons, resulting in reduced spiking activity rate and improved task performance.
Article
Computer Science, Artificial Intelligence
Yitao Ren, Peiyang Jin, Yiyang Li, Keming Mao
Summary: This paper proposes an effective ghost module based spectral network for hyperspectral image classification. It adopts Ghost3D module to reduce model parameter size by generating redundant feature maps with linear transformation. Ghost2D module with channel-wise attention is used to explore informative spectral feature representation. Compared with existing methods, the proposed approach achieves superior performance on three hyperspectral image datasets with fewer sample labelling and less resource consumption.
IET IMAGE PROCESSING
(2023)
Review
Neurosciences
Leila Bagheriye, Johan Kwisthout
Summary: The transition from conventional computing systems to harnessing the high parallelism of Bayesian inference has gained recent attention in the hardware implementation of Bayesian networks. Various implementations, including digital circuits, mixed-signal circuits, and analog circuits leveraging emerging nonvolatile devices, have been proposed. A futuristic overview is also provided to address existing hardware implementation problems.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Evolutionary Biology
Richard A. Watson, Rob Mills, C. L. Buckley, Kostas Kouvaris, Adam Jackson, Simon T. Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios, Daniel Power
EVOLUTIONARY BIOLOGY
(2016)
Review
Mathematics, Interdisciplinary Applications
Christopher L. Buckley, Chang Sub Kim, Simon McGregor, Anil K. Seth
JOURNAL OF MATHEMATICAL PSYCHOLOGY
(2017)
Article
Computer Science, Artificial Intelligence
Richard A. Watson, Rob Mills, C. L. Buckley
Article
Computer Science, Artificial Intelligence
Richard A. Watson, Rob Mills, C. L. Buckley
Article
Computer Science, Artificial Intelligence
Adam P. Davies, Richard A. Watson, Rob Mills, C. L. Buckley, Jason Noble
Article
Biology
Christopher L. Buckley, Seth Bullock
Article
Neurosciences
Christopher L. Buckley, Thomas Nowotny
Article
Mathematics, Interdisciplinary Applications
Philip Husbands, Andrew Philippides, Patricia Vargas, Christopher L. Buckley, Peter Fine, Ezequiel Di Paolo, Michael O'Shea
Article
Mathematics, Interdisciplinary Applications
Christopher L. Buckley, Seth Bullock, Lionel Barnett
Article
Mathematics, Interdisciplinary Applications
Richard A. Watson, C. L. Buckley, Rob Mills
Article
Physics, Fluids & Plasmas
L. Barnett, C. L. Buckley, S. Bullock
Article
Physics, Multidisciplinary
Christopher L. Buckley, Thomas Nowotny
PHYSICAL REVIEW LETTERS
(2011)
Article
Multidisciplinary Sciences
Andrei Zavada, Christopher L. Buckley, Dominique Martinez, Jean-Pierre Rospars, Thomas Nowotny
Article
Multidisciplinary Sciences
Christopher A. Harris, Christopher L. Buckley, Thomas Nowotny, Peter A. Passaro, Anil K. Seth, Gyoergy Kemenes, Michael O'Shea