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2. Augmenting Prototype Network with TransMix for Few-shot Hyperspectral Image Classification

Author

Liu, Chun, Yang, Longwei, Dong, Dongmei, Li, Zheng, Yang, Wei, Han, Zhigang, and Wang, Jiayao

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Few-shot hyperspectral image classification aims to identify the classes of each pixel in the images by only marking few of these pixels. And in order to obtain the spatial-spectral joint features of each pixel, the fixed-size patches centering around each pixel are often used for classification. However, observing the classification results of existing methods, we found that boundary patches corresponding to the pixels which are located at the boundary of the objects in the hyperspectral images, are hard to classify. These boundary patchs are mixed with multi-class spectral information. Inspired by this, we propose to augment the prototype network with TransMix for few-shot hyperspectrial image classification(APNT). While taking the prototype network as the backbone, it adopts the transformer as feature extractor to learn the pixel-to-pixel relation and pay different attentions to different pixels. At the same time, instead of directly using the patches which are cut from the hyperspectral images for training, it randomly mixs up two patches to imitate the boundary patches and uses the synthetic patches to train the model, with the aim to enlarge the number of hard training samples and enhance their diversity. And by following the data agumentation technique TransMix, the attention returned by the transformer is also used to mix up the labels of two patches to generate better labels for synthetic patches. Compared with existing methods, the proposed method has demonstrated sate of the art performance and better robustness for few-shot hyperspectral image classification in our experiments.

Published
2024

3. Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification

Author

Liu, Chun, Yang, Longwei, Li, Zheng, Yang, Wei, Han, Zhigang, Guo, Jianzhong, and Yu, Junyong

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Cross-domain few-shot hyperspectral image classification focuses on learning prior knowledge from a large number of labeled samples from source domains and then transferring the knowledge to the tasks which contain few labeled samples in target domains. Following the metric-based manner, many current methods first extract the features of the query and support samples, and then directly predict the classes of query samples according to their distance to the support samples or prototypes. The relations between samples have not been fully explored and utilized. Different from current works, this paper proposes to learn sample relations on different levels and take them into the model learning process, to improve the cross-domain few-shot hyperspectral image classification. Building on current method of "Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification" which adopts a domain discriminator to deal with domain-level distribution difference, the proposed method applies contrastive learning to learn the class-level sample relations to obtain more discriminable sample features. In addition, it adopts a transformer based cross-attention learning module to learn the set-level sample relations and acquire the attention from query samples to support samples. Our experimental results have demonstrated the contribution of the multi-level relation learning mechanism for few-shot hyperspectral image classification when compared with the state of the art methods.

Published
2023

5. Multi-level Cross-modal Feature Alignment via Contrastive Learning towards Zero-shot Classification of Remote Sensing Image Scenes

Author

Liu, Chun, Ma, Suqiang, Li, Zheng, Yang, Wei, and Han, Zhigang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Zero-shot classification of image scenes which can recognize the image scenes that are not seen in the training stage holds great promise of lowering the dependence on large numbers of labeled samples. To address the zero-shot image scene classification, the cross-modal feature alignment methods have been proposed in recent years. These methods mainly focus on matching the visual features of each image scene with their corresponding semantic descriptors in the latent space. Less attention has been paid to the contrastive relationships between different image scenes and different semantic descriptors. In light of the challenge of large intra-class difference and inter-class similarity among image scenes and the potential noisy samples, these methods are susceptible to the influence of the instances which are far from these of the same classes and close to these of other classes. In this work, we propose a multi-level cross-modal feature alignment method via contrastive learning for zero-shot classification of remote sensing image scenes. While promoting the single-instance level positive alignment between each image scene with their corresponding semantic descriptors, the proposed method takes the cross-instance contrastive relationships into consideration,and learns to keep the visual and semantic features of different classes in the latent space apart from each other. Extensive experiments have been done to evaluate the performance of the proposed method. The results show that our proposed method outperforms state of the art methods for zero-shot remote sensing image scene classification. All the code and data are available at github https://github.com/masuqiang/MCFA-Pytorch

Published
2023

8. Circulating tumor DNA-based stratification strategy for chemotherapy plus PD-1 inhibitor in advanced non-small-cell lung cancer

Author

Xu, Jiachen, Wan, Rui, Cai, Yiran, Cai, Shangli, Wu, Lin, Li, Baolan, Duan, Jianchun, Cheng, Ying, Li, Xiaoling, Wang, Xicheng, Han, Liang, Wu, Xiaohong, Fan, Yun, Yu, Yan, Lv, Dongqing, Shi, Jianhua, Huang, Jianjin, Zhou, Shaozhang, Han, Baohui, Sun, Guogui, Guo, Qisen, Ji, Youxin, Zhu, Xiaoli, Hu, Sheng, Zhang, Wei, Wang, Qiming, Jia, Yuming, Wang, Ziping, Song, Yong, Wu, Jingxun, Shi, Meiqi, Li, Xingya, Han, Zhigang, Liu, Yunpeng, Yu, Zhuang, Liu, An-Wen, Wang, Xiuwen, Zhou, Caicun, Zhong, Diansheng, Miao, Liyun, Zhang, Zhihong, Zhao, Hui, Yang, Jun, Wang, Dong, Wang, Yingyi, Li, Qiang, Zhang, Xiaodong, Ji, Mei, Yang, Zhenzhou, Cui, Jiuwei, Gao, Beili, Wang, Buhai, Liu, Hu, Nie, Lei, He, Mei, Jin, Shi, Gu, Wei, Shu, Yongqian, Zhou, Tong, Feng, Jian, Yang, Xinmei, Huang, Cheng, Zhu, Bo, Yao, Yu, Yu, Jianjun, Yao, Sheng, Shen, Runxi, Wang, Zhijie, and Wang, Jie

Published
2024
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14. Sunvozertinib for patients in China with platinum-pretreated locally advanced or metastatic non-small-cell lung cancer and EGFR exon 20 insertion mutation (WU-KONG6): single-arm, open-label, multicentre, phase 2 trial

Author

Wang, Mengzhao, Fan, Yun, Sun, Meili, Wang, Yongsheng, Zhao, Yanqiu, Jin, Bo, Hu, Ying, Han, Zhigang, Song, Xia, Liu, Anwen, Tang, Kejing, Ding, Cuimin, Liang, Li, Wu, Lin, Gao, Junzhen, Wang, Jianghong, Cheng, Ying, Zhou, Jianying, He, Yong, Dong, Xiaorong, Yao, Yu, Yu, Yan, Wang, Huijie, Sun, Si, Huang, Jianan, Fang, Jian, Li, Wen, Wang, Lin, Ren, Xiubao, Zhou, Chengzhi, Hu, Yanping, Zhao, Dahai, Yang, Runxiang, Xu, Fei, Huang, Yijiang, Pan, Yueyin, Cui, Jiuwei, Xu, Yan, Yang, Zhenfan, and Shi, Yuankai

Published
2024
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17. Periodic Orbits on Obtuse Edge Tessellating Polygons

Author

Baer, Benjamin R., Gilani, Faheem, Han, Zhigang, and Umble, Ronald

Subjects
Mathematics - Dynamical Systems
Abstract

A periodic orbit on a frictionless billiard table is a piecewise linear path of a billiard ball that begins and ends at the same point with the same angle of incidence. The period of a primitive periodic orbit is the number of times the ball strikes a side of the table as it traverses its trajectory exactly once. In this paper we find and classify the periodic orbits on a billiard table in the shape of a 120-isosceles triangle, a 60-rhombus, a 60-90-120-kite, and a 30-right triangle. In each case, we use the edge tessellation (also known as tiling) of the plane generated by the figure to unfold a periodic orbit into a straight line segment and to derive a formula for its period in terms of the initial angle and initial position., Comment: New Version (Submitted to PME)

Published
2019

18. Befotertinib (D-0316) versus icotinib as first-line therapy for patients with EGFR-mutated locally advanced or metastatic non-small-cell lung cancer: a multicentre, open-label, randomised phase 3 study

Author

Lu, Shun, Zhou, Jianying, Jian, Hong, Wu, Lin, Cheng, Ying, Fan, Yun, Fang, Jian, Chen, Gongyan, Zhang, Zhihong, Lv, Dongqing, Jiang, Liyan, Wu, Rong, Jin, Xiangming, Zhang, Xiaodong, Zhang, Junhong, Xie, Conghua, Sun, Gengyun, Huang, Dongning, Cui, Jiuwei, Guo, Renhua, Han, Zhigang, Chen, Zhendong, Liang, Jin, Zhuang, Wu, Hu, Xingsheng, Zang, Aimin, Zhang, Yi, Cang, Shundong, Lan, Yuanbo, Chen, Xi, Liu, Laiyu, Li, Xingya, Chen, Jun, Ma, Rui, Guo, Yanzhen, Sun, Ping, Tian, Panwen, Pan, Yueyin, Liu, Zhe, Cao, Peiguo, Ding, Lieming, Wang, Yang, Yuan, Xiaobin, and Wu, Pengxiang

Published
2023
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21. Central nervous system efficacy of rezivertinib (BPI-7711) in advanced NSCLC patients with EGFR T790M mutation: A pooled analysis of two clinical studies

Author

Yang, Sheng, Wu, Shiman, Zhao, Yanqiu, Chen, Gongyan, Zhu, Bo, Li, Xingya, Wang, Ke, Shi, Jianhua, Cang, Shundong, Yao, Wenxiu, Fan, Yun, Fang, Jian, Zhang, Liangming, Zhou, Jianying, Wu, Lin, Zheng, Rongsheng, Huang, Meijuan, Pan, Yueyin, Yang, Zhixiong, Sun, Meili, Yu, Huiqing, Wang, Donglin, Huang, Jianan, Wang, Lijun, Shu, Yongqian, Chen, Zhaohong, Liu, Chunling, Li, Jingzhang, Liu, Jiwei, Sun, Shenghua, Guo, Yanzhen, Meng, Zili, Liu, Zhefeng, Han, Zhigang, Wu, Gang, Lu, Hong, Ma, Rui, Hu, Sheng, Zhao, Guofang, Zhang, Longzhen, Liu, Zheng, Xie, Congying, Zhong, Diansheng, Zhao, Hui, Bi, Minghong, Yi, Shanyong, Guo, Shuliang, Yi, Tienan, Li, Wen, Lin, Yingcheng, Chen, Zhendong, Zhuang, Zhixiang, Guo, Zhongliang, Greco, Michael, Wang, Tingting, Zhou, Anqi, and Shi, Yuankai

Published
2023
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27. Efficacy and Safety of Rezivertinib (BPI-7711) in Patients With Locally Advanced or Metastatic/Recurrent EGFR T790M-Mutated NSCLC: A Phase 2b Study

Author

Shi, Yuankai, Wu, Shiman, Wang, Ke, Cang, Shundong, Yao, Wenxiu, Fan, Yun, Wu, Lin, Huang, Meijuan, Li, Xingya, Pan, Yueyin, Yang, Zhixiong, Zhu, Bo, Chen, Gongyan, Shi, Jianhua, Sun, Meili, Fang, Jian, Wang, Lijun, Chen, Zhaohong, Liu, Chunling, Li, Jingzhang, Liu, Jiwei, Sun, Shenghua, Zhao, Yanqiu, Guo, Yanzhen, Meng, Zili, Liu, Zhefeng, Han, Zhigang, Lu, Hong, Ma, Rui, Hu, Sheng, Zhao, Guofang, Liu, Zheng, Xie, Congying, Zhong, Diansheng, Zhao, Hui, Yu, Huiqing, Zhang, Longzhen, Bi, Minghong, Yi, Shanyong, Guo, Shuliang, Yi, Tienan, Li, Wen, Lin, Yingcheng, Shu, Yongqian, Chen, Zhendong, Guo, Zhongliang, Greco, Michael, Wang, Tingting, and Shen, Haijiao

Published
2022
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29. The global burden of disease attributable to high body mass index in 204 countries and territories: Findings from 1990 to 2019 and predictions to 2035.

Author

Chen, Yuhan, Ma, Li, Han, Zhigang, and Xiong, Peng

Subjects
GLOBAL burden of disease, BODY mass index, DEATH rate, MOVING average process, DEMOGRAPHIC change
Abstract

Aim: Our study aims to provide an updated estimate of age‐ and sex‐specific deaths and disability‐adjusted life years (DALYs) associated with high body mass index (BMI) from 1990 to 2019 at the global, regional and national levels, and to forecast the global burden of disease attributed to high BMI from 2020 to 2035. Methods: We used the data for the number of deaths, DALYs, age‐standardized rate (per 100 000 population), percentage change and population attributable fraction from the Global Burden of Disease Study 2019 (GBD 2019) to examine the disease burden attributable to high BMI. We further applied an autoregressive integrated moving average (ARIMA) model to predict the disease burden for the period 2020‐2035. Results: From 1990 to 2019, the deaths and DALYs attributable to high BMI increased by 148% and 155.86% for men, and by 111.67% and 121.78% for women, respectively. In 2019, high BMI directly accounted for 8.52% [95% uncertainty intervals (UI) 0.05, 0.12] of all‐cause deaths and 5.89% (95% UI 0.04, 0.08) of global DALYs. The highest death rates were observed in men aged 65‐69 and women aged 75‐79. The highest DALY rates were observed in the age group of 60‐64 for both sexes. In 2019, the highest age‐standardized deaths and DALY rates were observed in the Central Asia region [163.15 (95% UI 107.72, 223.58) per 100 000 people] and the Oceania region [4643.33 (95% UI 2835.66, 6902.6) per 100 000 people], respectively. Fiji [319.08 (95% UI 213.77, 444.96) per 100 000 people] and Kiribati [10 000.58 (95% UI 6266.55, 14159.2) per 100 000 people] had the highest age‐standardized deaths and DALY rates, respectively. In 2019, the highest age‐standardized rates of high BMI‐related deaths and DALYs were observed in the middle‐high socio‐demographic index quintile and in the middle socio‐demographic index quintile. The age‐standardized deaths and DALY rates attributable to high BMI are projected to increase in both sexes from 2020 to 2035. The death rates are projected to rise from 62.79 to 64.31 per 100 000 people, while the DALY rates are projected to rise from 1946 to 2099.54 per 100 000 people. Conclusions: High BMIs significantly contribute to the global disease burden. The projected rise in deaths and DALY rates attributable to high BMI by 2035 highlights the critical need to address the impact of obesity on public health. Our study provides policymakers with up‐to‐date and comprehensive information. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Once-daily savolitinib in Chinese patients with pulmonary sarcomatoid carcinomas and other non-small-cell lung cancers harbouring MET exon 14 skipping alterations: a multicentre, single-arm, open-label, phase 2 study

Author

Lu, Shun, Fang, Jian, Li, Xingya, Cao, Lejie, Zhou, Jianying, Guo, Qisen, Liang, Zongan, Cheng, Ying, Jiang, Liyan, Yang, Nong, Han, Zhigang, Shi, Jianhua, Chen, Yuan, Xu, Hua, Zhang, Helong, Chen, Gongyan, Ma, Rui, Sun, Sanyuan, Fan, Yun, Li, Jing, Luo, Xian, Wang, Linfang, Ren, Yongxin, and Su, Weiguo

Published
2021
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35. The origin and spread of HIV‐1 CRF59_01B epidemic in China: A molecular network and phylogeographic analysis.

Author

Yan, Huanchang, Wu, Hao, Li, Shunming, Wang, Jiahang, Luo, Yefei, Luo, Rui, Gu, Yuzhou, Cai, Yanshan, Tang, Shixing, Hao, Yuantao, Gu, Jing, Han, Zhigang, and Liu, Yu

Subjects
HIV, BAYESIAN analysis, EPIDEMICS, INFORMATION sharing, DATABASES
Abstract

Human immunodeficiency virus type 1 CRF59_01B, identified in China in 2013, has been detected nationwide, exhibiting notably high prevalence in Guangzhou and its vicinity. This study aimed to unravel its origin and migration. A data set was established, incorporating all available CRF59_01B pol gene sequences and their metadata from Guangzhou and the public database. Bayesian phylogeographic analysis demonstrated that CRF59_01B originated in Shenzhen, the neighboring city of Guangzhou, around 1998 with posterior probability of 0.937. Molecular network analysis detected 1131 transmission links and showed a remarkably high clustering rate (78.9%). Substantial inter‐city transmissions (26.5%, 300/1131) were observed between Shenzhen and Guangzhou while inter‐region transmissions linked Guangzhou with South (46) and Southwest (64) China. The centre of Guangzhou was the hub of CRF59_01B transmission, including the inflow from Shenzhen (3.57 events/year) and outflow to the outskirts of Guangzhou (>2 events/year). The large‐scale analysis revealed significant migration from Shenzhen to Guangzhou (5.08 events/year) and North China (0.59 events/year), and spread from Guangzhou to Central (0.47 events/year), East (0.42 events/year), South (0.76 events/year), Southwest China (0.76 events/year) and Shenzhen (1.89 events/year). Shenzhen and Guangzhou served as the origin and the hub of CRF59_01B circulation, emphasizing inter‐city cooperation and data sharing to confine its nationwide diffusion. [ABSTRACT FROM AUTHOR]

Published
2024
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37. On-Orbit Polarization Calibration of the Multi-angle Polarization Imager Based on Sunglint Over the Ocean

Author

Guo, Junjie, Yao, Zhigang, Han, Zhigang, Zhao, Zengliang, Jiang, Jun, Yan, Wei, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Gu, Yidong, editor, Gao, Ming, editor, and Zhao, Guangheng, editor

Published
2019
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38. Cross-Calibration of S-NPP/VIIRS and Tiangong-2/MAI Visible Channels Using the SNO Method

Author

Jiang, Jun, Han, Zhigang, Yao, Zhigang, Zhao, Zengliang, Guo, Junjie, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Gu, Yidong, editor, Gao, Ming, editor, and Zhao, Guangheng, editor

Published
2019
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41. Sunvozertinib for patients in China with platinum-pretreated locally advanced or metastatic non-small-cell lung cancer and EGFR exon 20 insertion mutation (WU-KONG6): single-arm, open-label, multicentre, phase 2 trial

Author

Wang, Mengzhao, primary, Fan, Yun, additional, Sun, Meili, additional, Wang, Yongsheng, additional, Zhao, Yanqiu, additional, Jin, Bo, additional, Hu, Ying, additional, Han, Zhigang, additional, Song, Xia, additional, Liu, Anwen, additional, Tang, Kejing, additional, Ding, Cuimin, additional, Liang, Li, additional, Wu, Lin, additional, Gao, Junzhen, additional, Wang, Jianghong, additional, Cheng, Ying, additional, Zhou, Jianying, additional, He, Yong, additional, Dong, Xiaorong, additional, Yao, Yu, additional, Yu, Yan, additional, Wang, Huijie, additional, Sun, Si, additional, Huang, Jianan, additional, Fang, Jian, additional, Li, Wen, additional, Wang, Lin, additional, Ren, Xiubao, additional, Zhou, Chengzhi, additional, Hu, Yanping, additional, Zhao, Dahai, additional, Yang, Runxiang, additional, Xu, Fei, additional, Huang, Yijiang, additional, Pan, Yueyin, additional, Cui, Jiuwei, additional, Xu, Yan, additional, Yang, Zhenfan, additional, and Shi, Yuankai, additional

Published
2023
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42. Constructing the fixed point of a non-isometric plane similarity

Author

Han, Zhigang and Umble, Ron

Subjects
Mathematics - History and Overview, 51M15 (Primary) 51M04 (Secondary)
Abstract

We present an algorithm for constructing the fixed point of a general non-isometric similarity of the plane., Comment: 7 pages, 7 figures

Published
2014

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