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Browsing by Author "Zhu, Lei"
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Item Cycle-consistent Conditional Adversarial Transfer Networks(ACM, 2019-10) Li, Jingjing; Chen, Erpeng; Ding, Zhengming; Zhu, Lei; Lu, Ke; Huang, Zi; Computer Information and Graphics Technology, School of Engineering and TechnologyDomain adaptation investigates the problem of cross-domain knowledge transfer where the labeled source domain and unlabeled target domain have distinctive data distributions. Recently, adversarial training have been successfully applied to domain adaptation and achieved state-of-the-art performance. However, there is still a fatal weakness existing in current adversarial models which is raised from the equilibrium challenge of adversarial training. Specifically, although most of existing methods are able to confuse the domain discriminator, they cannot guarantee that the source domain and target domain are sufficiently similar. In this paper, we propose a novel approach named cycle-consistent conditional adversarial transfer networks (3CATN) to handle this issue. Our approach takes care of the domain alignment by leveraging adversarial training. Specifically, we condition the adversarial networks with the cross-covariance of learned features and classifier predictions to capture the multimodal structures of data distributions. However, since the classifier predictions are not certainty information, a strong condition with the predictions is risky when the predictions are not accurate. We, therefore, further propose that the truly domain-invariant features should be able to be translated from one domain to the other. To this end, we introduce two feature translation losses and one cycle-consistent loss into the conditional adversarial domain adaptation networks. Extensive experiments on both classical and large-scale datasets verify that our model is able to outperform previous state-of-the-arts with significant improvements.Item Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort(Oxford University Press, 2019-07) Du, Lei; Liu, Kefei; Zhu, Lei; Yao, Xiaohui; Risacher, Shannon L.; Guo, Lei; Saykin, Andrew J.; Shen, Li; Radiology & Imaging Sciences, IU School of MedicineMotivation Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted. Results We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer’s Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression. Availability and implementation The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA. Supplementary information Supplementary data are available at Bioinformatics online.Item Leveraging the Invariant Side of Generative Zero-Shot Learning(IEEE, 2019) Li, Jingjing; Jing, Mengmeng; Lu, Ke; Ding, Zhengming; Zhu, Lei; Huang, Zi; Electrical and Computer Engineering, School of Engineering and TechnologyConventional zero-shot learning (ZSL) methods generally learn an embedding, e.g., visual-semantic mapping, to handle the unseen visual samples via an indirect manner. In this paper, we take the advantage of generative adversarial networks (GANs) and propose a novel method, named leveraging invariant side GAN (LisGAN), which can directly generate the unseen features from random noises which are conditioned by the semantic descriptions. Specifically, we train a conditional Wasserstein GANs in which the generator synthesizes fake unseen features from noises and the discriminator distinguishes the fake from real via a minimax game. Considering that one semantic description can correspond to various synthesized visual samples, and the semantic description, figuratively, is the soul of the generated features, we introduce soul samples as the invariant side of generative zero-shot learning in this paper. A soul sample is the meta-representation of one class. It visualizes the most semantically-meaningful aspects of each sample in the same category. We regularize that each generated sample (the varying side of generative ZSL) should be close to at least one soul sample (the invariant side) which has the same class label with it. At the zero-shot recognition stage, we propose to use two classifiers, which are deployed in a cascade way, to achieve a coarse-to-fine result. Experiments on five popular benchmarks verify that our proposed approach can outperform state-of-the-art methods with significant improvements.Item Maximum Density Divergence for Domain Adaptation(IEEE, 2021) Li, Jingjing; Chen, Erpeng; Ding, Zhengming; Zhu, Lei; Lu, Ke; Shen, Heng Tao; Computer Information and Graphics Technology, School of Engineering and TechnologyUnsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two domains. The state-of-the-art methods practice this very idea by either conducting adversarial training or minimizing a metric which defines the distribution gaps. In this paper, we propose a new domain adaptation method named adversarial tight match (ATM) which enjoys the benefits of both adversarial training and metric learning. Specifically, at first, we propose a novel distance loss, named maximum density divergence (MDD), to quantify the distribution divergence. MDD minimizes the inter-domain divergence ("match" in ATM) and maximizes the intra-class density ("tight" in ATM). Then, to address the equilibrium challenge issue in adversarial domain adaptation, we consider leveraging the proposed MDD into adversarial domain adaptation framework. At last, we tailor the proposed MDD as a practical learning loss and report our ATM. Both empirical evaluation and theoretical analysis are reported to verify the effectiveness of the proposed method. The experimental results on four benchmarks, both classical and large-scale, show that our method is able to achieve new state-of-the-art performance on most evaluations.Item Mechanical loading attenuates breast cancer-associated bone metastasis in obese mice by regulating the bone marrow microenvironment(Wiley, 2021) Huang, Menglu; Liu, Hong; Zhu, Lei; Li, Xinle; Li, Jie; Yang, Shuang; Liu, Daquan; Song, Xiaomeng; Yokota, Hiroki; Zhang, Ping; Biomedical Engineering, School of Engineering and TechnologyBreast cancer, a common malignancy for women, preferentially metastasizes to bone and obesity elevates the chance of its progression. While mechanical loading can suppress obesity and tumor-driven osteolysis, its effect on bone-metastasized obese mice has not been investigated. Here, we hypothesized that mechanical loading can lessen obesity-associated bone degradation in tumor-invaded bone by regulating the fate of bone marrow-derived cells. In this study, the effects of mechanical loading in obese mice were evaluated through X-ray imaging, histology, cytology, and molecular analyses. Tumor inoculation to the tibia elevated body fat composition, osteolytic lesions, and tibia destruction, and these pathologic changes were stimulated by the high-fat diet (HFD). However, mechanical loading markedly reduced these changes. It suppressed osteoclastogenesis by downregulating receptor activator of nuclear factor Kappa-B ligand and cathepsin K and promoted osteogenesis, which was associated with the upregulation of OPG and downregulation of C/enhancer-binding protein alpha and proliferator-activated receptor gamma for adipogenic differentiation. Furthermore, it decreased the levels of tumorigenic genes such as Rac1, MMP9, and interleukin 1β. In summary, this study demonstrates that although a HFD aggravates bone metastases associated with breast cancer, mechanical loading significantly protected tumor-invaded bone by regulating the fate of bone marrow-derived cells. The current study suggests that mechanical loading can provide a noninvasive, palliative option for alleviating breast cancer-associated bone metastasis, in particular for obese patients.