Article
Computer Science, Artificial Intelligence
Zhan Wang
Summary: This paper proposes an innovative method for accurately segmenting and reconstructing complex artistic images using an enhanced U-net segmentation framework and surface extraction image reconstruction algorithm. Empirical validation demonstrates the impressive performance of this method in terms of segmentation accuracy and reconstruction quality, enhancing artists' ability to discover and create high-quality new media artworks.
PEERJ COMPUTER SCIENCE
(2023)
Article
Computer Science, Interdisciplinary Applications
Chao Liu, Deli Wang, Han Zhang, Wei Wu, Wenzhi Sun, Ting Zhao, Nenggan Zheng
Summary: Reconstructing neuron morphologies from fluorescence microscope images is crucial for neuroscience studies. This study proposes a strategy of using two-stage generative models to simulate training data with voxel-level labels, resulting in realistic 3D images with underlying voxel labels. The results show that networks trained on synthetic data outperform those trained on manually labeled data in segmentation performance.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Computer Science, Information Systems
Neil Micallef, Dylan Seychell, Claude J. Bajada
Summary: The article introduces a novel adaptation of the U-Net++ model and demonstrates its excellent performance in brain tumor segmentation. The proposed approach differs from the standard U-Net++ model in terms of loss function, number of convolutional blocks, and deep supervision method. By implementing data augmentation and post-processing techniques, the model predictions were substantially improved.
Article
Computer Science, Interdisciplinary Applications
Bo Yang, Min Liu, Yaonan Wang, Kang Zhang, Erik Meijering
Summary: This paper presents a 3D neuron segmentation network called SGSNet that enhances weak neuronal structures and removes background noises. The network utilizes two decoding paths, one for acquiring segmentation maps and the other for detecting neuronal structures. A structure attention module is designed to integrate features and provide contextual guidance to improve segmentation performance.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Biochemical Research Methods
Qiufu Li, Linlin Shen
Summary: The paper introduces a 3D wavelet and deep learning-based method for neuron segmentation, utilizing 3D WaveUNet to process neuronal cubes and improve performance in noisy neuronal images. The integrated 3D wavelets efficiently assist in 3D neuron segmentation and reconstruction.
Article
Public, Environmental & Occupational Health
Sanchit Vijay, Thejineaswar Guhan, Kathiravan Srinivasan, P. M. Durai Raj Vincent, Chuan-Yu Chang
Summary: Brain tumor diagnosis has been time-consuming, but automating the segmentation process can speed it up. This paper introduces SPP-U-Net, a model that replaces residual connections with a combination of Spatial Pyramid Pooling (SPP) and Attention blocks, allowing for greater context and scope in the segmentation. The proposed approach achieves comparable results to existing literature without increasing training parameters.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Computer Science, Information Systems
Rui Kong, Xianyong Li, Jiankun Wang, Xiaoling Wang
Summary: By using the U-Net++ neural network model, the image segmentation of bacteria is improved, leading to higher accuracy and robustness in the formation of bacterial biofilms, even in cases of low contrast and noise. This method has great significance for understanding the growth of bacterial biofilms in extreme environments.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Xuyang Cao, Houjin Chen, Yanfeng Li, Yahui Peng, Shu Wang, Lin Cheng
Summary: This study proposes a method using dilated densely connected U-Net ((DU)-U-2-Net) combined with an uncertainty focus loss to accurately segment breast masses in a small ABUS dataset. Experimental results demonstrate that the proposed method outperforms existing methods on 3D ABUS mass segmentation tasks.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)
Article
Computer Science, Interdisciplinary Applications
Kisuk Lee, Ran Lu, Kyle Luther, H. Sebastian Seung
Summary: This study demonstrates that dense voxel embeddings learned through deep metric learning can accurately segment neurons from 3D electron microscopy images. A metric graph is constructed from the dense voxel embeddings generated by a convolutional network, and partitioning the graph with long-range edges as repulsive constraints results in precise initial segmentation, particularly useful for very thin objects. The convolutional embedding network is reused without modification to aggregate systematic splits caused by complex self-contact motifs, achieving state-of-the-art accuracy in reconstructing 3D neurons from brain images acquired through serial section electron microscopy. The proposed object-centered representation may have broader applications in automated neural circuit reconstruction.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Chemistry, Multidisciplinary
Ying Wang, Ki-Young Koo
Summary: This paper proposes a method to remove vegetation from 3D reconstructed point clouds obtained from photos taken by UAVs. The method uses a 2D image segmentation model and projection matrices. The method was successfully applied to a cut-slope in South Korea and showed promising results in removing vegetation.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Preetpal Kaur Buttar, Manoj Kumar Sachan
Summary: This paper proposes a deep learning algorithm for cloud segmentation on the Landsat 8 multispectral dataset. Experimental results show that the proposed model achieves higher accuracy and performance compared to other methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Biology
Hengfei Cui, Yan Li, Lei Jiang, Yifan Wang, Yong Xia, Yanning Zhang
Summary: This paper presents an automatic and accurate myocardial pathology segmentation framework based on the U-Net++ and EfficientSeg models. By utilizing a two-stage segmentation strategy and the Focal loss method, the proposed method achieves excellent performance in the Myocardial Pathology Segmentation Challenge. It can further facilitate myocardial pathology segmentation in medical practice.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Cameron Beeche, Jatin P. Singh, Joseph K. Leader, Naciye S. Gezer, Amechi P. Oruwari, Kunal K. Dansingani, Jay Chhablani, Jiantao Pu
Summary: This study developed and validated a novel convolutional neural network called Super U-Net for medical image segmentation. The Super U-Net integrates dynamic receptive field module and fusion upsampling module to improve image segmentation performance.
PATTERN RECOGNITION
(2022)
Article
Energy & Fuels
Han Gujing, Zhang Min, Wu Wenzhao, He Min, Liu Kaipei, Qin Liang, Liu Xia
Summary: This paper proposes an insulator segmentation method based on improved U-Net, which embeds the attention mechanism ECA-Net to enhance the model's ability in extracting semantic features and improving the accuracy of insulator detection. The experimental results demonstrate that the proposed method achieves an average overlap IOU of 96.8%, enabling more accurate segmentation of different types of insulators in complex backgrounds.
Article
Computer Science, Interdisciplinary Applications
Xiaodan Wei, Qinghao Liu, Min Liu, Yaonan Wang, Erik Meijering
Summary: In this paper, we propose a two-stage deep neural network for fast and accurate soma detection in large-scale and high-resolution whole mouse brain images. A lightweight Multi-level Cross Classification Network (MCC-Net) is first used to filter out images without somas and generate coarse candidate images, followed by a Scale Fusion Segmentation Network (SFS-Net) to accurately segment soma regions. Experimental results demonstrate excellent performance of the proposed method and a public dataset named WBMSD is established for further research on soma detection.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Correction
Microbiology
Lin-Lin Shen, Abdul Waheed, Yan-Ping Wang, Oswald Nkurikiyimfura, Zong-Hua Wang, Li-Na Yang, Jiasui Zhan
FRONTIERS IN MICROBIOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Yating Huang, Xuechen Li, Siting Zheng, Zhongliang Li, Sihan Li, Linlin Shen, Changen Zhou, Zhihui Lai
Summary: The size and shape of the tongue can reflect different pathological changes of the human body in Traditional Chinese Medicine (TCM). In this work, an efficient deep network, TSCWNet, is proposed for tongue size and shape classification. Experimental results demonstrate that the network achieves better classification performance for tongue diagnosis.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Ziqi Jin, Jinheng Xie, Bizhu Wu, Linlin Shen
Summary: In this paper, a weakly supervised pedestrian segmentation framework is proposed to directly generate the foreground mask from person re-identification datasets with only image-level subject ID labels. The Image Synthesis Augmentation (ISA) technique is also introduced to further enhance the dataset. Experimental results demonstrate that the proposed framework learns robust and discriminative features, achieving significant improvement in mAP compared to the baseline on widely used datasets including Market-1501, CUHK03, and MSMT17. The code will be made available soon.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Syed Furqan Qadri, Hongxiang Lin, Linlin Shen, Mubashir Ahmad, Salman Qadri, Salabat Khan, Maqbool Khan, Syeda Shamaila Zareen, Muhammad Azeem Akbar, Md Belal Bin Heyat, Saqib Qamar
Summary: This study proposes a patch-based deep learning approach for automatic CT vertebral segmentation. The method extracts discriminative features from unlabeled data using a stacked sparse autoencoder and achieves accurate segmentation of CT vertebrae.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zhongliang Li, Xuechen Li, Zhihao Jin, Linlin Shen
Summary: In this paper, a novel self-supervised pretraining method based on pseudo-lesion generation and restoration was proposed for COVID-19 diagnosis. The method trained an encoder-decoder architecture-based U-Net using pairs of pseudo-COVID-19 images and normal CT images for image restoration, and then fine-tuned the pretrained encoder using labeled data. Experimental results demonstrated that the proposed method extracted better feature representation for COVID-19 diagnosis, achieving higher accuracy compared to the supervised model pretrained on large-scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jiajun Wen, Honglin Chu, Zhihui Lai, Tianyang Xu, Linlin Shen
Summary: This paper proposes an innovative method called EFSCF, which performs jointly sparse feature learning to handle the spatial boundary effect effectively while suppressing the influence of background pixels and noises. The proposed method achieves better tracking performance than the state-of-the-art trackers by exploring the structural sparsity in rows and columns of a learned filter simultaneously.
Article
Neurosciences
Xiuzhi Zhao, Wenting Chen, Weicheng Xie, Linlin Shen
Summary: This study proposes a Style Attention based Global-local Aware GAN to generate personalized caricatures. It integrates the facial characteristics of a subject through a landmark-based warp controller for personalized shape exaggeration and uses a style-attention module for appropriate fusion of facial features and caricature style. The results indicate that the proposed method can preserve the identity of input photos and generate caricatures close to those drawn by real artists.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Plant Sciences
Linlin Shen, Haiyan Deng, Ganglong Zhang, Anqi Ma, Xiaoyong Mo
Summary: Climate warming poses a significant threat to global ecosystems, impacting the geographic distribution and suitable growth areas of species. This study focused on predicting the potential cultivation regions of Castanopsis hystrix Miq., a research object, based on the MaxEnt model and environmental variables. The key factors affecting the distribution area of C. hystrix Miq. were identified as the minimum temperature of the coldest month, precipitation of the driest month, and precipitation of the warmest quarter. The suitable cultivation regions were found in central and southern China, with a range of 18-34°N and 89-122°E, covering an area of 261.95 x 10(4) km(2). Under different climate scenarios, the spatial pattern of C. hystrix Miq. will migrate to different regions, with varying changes in suitable area and cultivation distribution.
Article
Computer Science, Artificial Intelligence
Ya-Nan Zhang, Linlin Shen, Zhihui Lai
Summary: In the field of computer vision, removing rain streaks from images is an important task as it affects the performance and quality of subsequent tasks and outdoor images. Deep learning-based methods have been proposed to address this issue, but they lack interpretability and have limited performance in detail restoration and rain streak removal. This paper introduces a rain streaks model-driven deep network, MSANet, to overcome these limitations.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Junhong Zhang, Zhihui Lai, Heng Kong, Linlin Shen
Summary: In this paper, a new robust manifold twin bounded SVM (RMTBSVM) algorithm is proposed, which considers both robustness and discriminability. By using the capped L-1-norm as the distance metric and adding robust manifold regularization, the robustness and classification performance are improved. The algorithm is extended for nonlinear classification using the kernel method.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Siyang Song, Shashank Jaiswal, Enrique Sanchez, Georgios Tzimiropoulos, Linlin Shen, Michel Valstar
Summary: This article addresses two important issues in automatic personality analysis systems: the use of short video segments or single frames for inferring personality traits, and the lack of methods for encoding person-specific facial dynamics. To tackle these issues, the paper proposes a novel Rank Loss for self-supervised learning of facial dynamics and a method to represent person-specific dynamics. The approach achieves promising results in personality estimation and shows the importance of the tasks performed by the subject in the video and the use of multi-scale dynamics.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Thermodynamics
Lili Liu, Cai Chen, Linlin Shen, Gang Xu, Yufeng Wen, Xianshi Zing
Summary: The pressure dependence of lattice and elastic constants of g-TiAl, DO22-Al3Ti, and alpha(2)-Ti3Al binary precipitates was investigated using a first-principles approach. The calculated results at 0 GPa and 0 K were in good agreement with existing experimental and theoretical values. The temperature and pressure dependencies of bulk modulus, Gibbs free energy, thermal expansion coefficient, and heat capacity at constant pressure were systematically studied using density-functional perturbation theory (DFPT) under the quasiharmonic approximation (QHA) in the ranges of 0-1000 K and 0-30 GPa.
HIGH TEMPERATURES-HIGH PRESSURES
(2023)
Article
Computer Science, Artificial Intelligence
Yang Zhang, Linlin Shen
Summary: In this study, an automatic learning rate tuning method for memristive deep learning systems is presented. The method utilizes memristors to adjust the adaptive learning rate in deep neural networks. The proposed method is robust to noisy gradients, various architectures, and different datasets and can address the issue of over-fitting. Moreover, a quantized neural network architecture is utilized in the presented system, leading to an increase in training efficiency without the loss of testing accuracy.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zhe Kong, Wentian Zhang, Feng Liu, Wenhan Luo, Haozhe Liu, Linlin Shen, Raghavendra Ramachandra
Summary: This study empirically proves the importance of model initialization for generalization and proposes a self-supervised learning-based method to address the issue of unknown PAI.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Feng Liu, Zhe Kong, Haozhe Liu, Wentian Zhang, Linlin Shen
Summary: This paper proposes a channel-wise feature denoising fingerprint presentation attack detection method that learns important features of fingerprint images by weighting the importance of each channel and suppressing the propagation of noise channels. Experimental results show that the proposed method achieves high accuracy and true detection rate.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2022)
