|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | EBV-Induced Human CD8^+ NKT Cells Synergise CD4^+ NKT Cells Suppressing EBV-Associated Tumours upon Induction of Thl-Bias显示文摘 | Wei Xiao Li Li Rui Zhou Ruijing Xiao Yujuan Wang Xiang Ji Mengjun Wu Lan Wang Wei Huang Xiaoling Zheng Xinti Tan Lang Chen Tao Xiong Jie Xiong Youxin Jin Jinquan Tan Yuling He | 2009 | Cellular & Molecular Immunology2009，6，5： | 5 |
| 2 | All roads lead to Rome: pathways of NKT cells promoting asthma显示文摘 | Tan Jinquan Wang Li He Yuling Chen Lang | 2006 | Archivum Immunologiae et Therapiae Experimentalis2006，，5： | 1 |
| 3 | Material-SAM:Adapting SAM for Material XCT显示文摘X-ray Computed Tomography(XCT)enables non-destructive acquisition of the internal structure of materials,and image segmentation plays a crucial role in analyzing material XCT images.This paper proposes an image segmentation method based on the Segment Anything model(SAM).We constructed a dataset of carbide in nickel-based single crystal superalloys XCT images and preprocessed the images using median filtering,histogram equalization,and gamma correction.Subsequently,SAM was fine-tuned to adapt to the task of material XCT image segmentation,resulting in Material-SAM.We compared the performance of threshold segmentation,SAM,U-Net model,and Material-SAM.Our method achieved 88.45%Class Pixel Accuracy(CPA)and 88.77%Dice Similarity Coefficient(DSC)on the test set,outperforming SAM by 5.25%and 8.81%,respectively,and achieving the highest evaluation.Material-SAM demonstrated lower input requirements compared to SAM,as it only required three reference points for completing the segmentation task,which is one-fifth of the requirement of SAM.Material-SAM exhibited promising results,highlighting its potential as a novel method for material XCT image segmentation. | Xuelong Wu Junsheng Wang Zhongyao Li Yisheng Miao Chengpeng Xue Yuling Lang Decai Kong Xiaoying Ma Haibao Qiao | 2024 | Computers, Materials & Continua2024，78，3： | 0 |
| 4 | AtomGAN:unsupervised deep learning for fast and accurate defect detection of 2D materials at the atomic scale显示文摘The extraction of atomic-level material features from electron microscope images is crucial for studying structure-property relationships and discovering new materials.However,traditional electron microscope analyses rely on time-consuming and complex human operations;thus,they are only applicable to images with a small number of atoms.In addition,the analysis results vary due to observers’individual deviations.Although efforts to introduce automated methods have been performed previously,many of these methods lack sufficient labeled data or require various conditions in the detection process that can only be applied to the target material.Thus,in this study,we developed AtomGAN,which is a robust,unsupervised learning method,that segments defects in classical 2D material systems and the heterostructures of MoS_(2)/WS_(2)automatically.To solve the data scarcity problem,the proposed model is trained on unpaired simulated data that contain point and line defects for MoS_(2)/WS_(2).The proposed AtomGAN was evaluated on both simulated and real electron microscope images.The results demonstrate that the segmented point defects and line defects are presented perfectly in the resulting figures,with a measurement precision of 96.9%.In addition,the cycled structure of AtomGAN can quickly generate a large number of simulated electron microscope images. | Danpeng CHENG Wuxin SHA Zuo XU Shide LI Zhigao YIN Yuling LANG Shun TANG Yuan-Cheng CAO | 2023 | Science China(Information Sciences)2023，66，6： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员