公开(公告)号：US20220027786A1

公开(公告)日：2022-01-27

申请号：US16947234

申请日：2020-07-24

Applicant: Macau University of Science and Technology

Inventor： Yong LIANG ,  Ziyi YANG

IPC: G06N20/00 ,  G06K9/62 ,  G06F17/16

Abstract: A robust multimodal data integration method, termed the SMSPL technique, aimed at simultaneously predicting subtypes of cancers and identifying potentially significant multiomics signatures, is provided. The SMSPL technique leverages linkages among different types of data to interactively recommend high-confidence training samples during classifier training. Particularly, a new soft weighting scheme is adopted to assign weights to training samples of each type, thus more faithfully reflecting latent importance of samples in self-paced learning. The SMSPL technique iterates between calculating the sample weights from training loss values and minimizing weighted training losses for classifier updating, allowing the classifiers to be efficiently trained. In classifying a test sample, outputs of the trained classifiers are integrated to yield a class label by solving an optimization problem for minimizing a sum of classifier losses in selecting a candidate class label, making the SMSPL technique more accruable to discriminate equivocal samples.

公开(公告)号：US20170169563A1

公开(公告)日：2017-06-15

申请号：US14965918

申请日：2015-12-11

Applicant: Macau University of Science and Technology

Inventor： Yong LIANG ,  Liang-Yong XIA ,  Xu-Xin LIN ,  Xiao-Ying LIU ,  Kuok-Fan CHAN

IPC: G06T7/00 ,  A61B5/055 ,  G06K9/52 ,  H04N19/85 ,  G06T11/00 ,  G06K9/62

CPC classification number: A61B5/055 ,  A61B5/0044 ,  A61B5/0263 ,  G01R33/5608 ,  G01R33/5611 ,  G01R33/56308 ,  G01R33/56366 ,  H04N19/59 ,  H04N19/85

Abstract: A method for determining a background component and a dynamic component of an image frame from an under-sampled data sequence obtained in a dynamic MRI application is provided. The two components are determined by optimizing a low-rank component and a sparse component of the image frame in a sense of minimizing a weighted sum of terms. The terms include a Schattenp=1/2 (S1/2-norm) of the low-rank component, an L1/2-norm of the sparse component additionally sparsified by a sparsifying transform, and an L2-norm of a difference between the sensed data sequence and a reconstructed data sequence. The reconstructed one is obtained by sub-sampling the image frame according to an encoding or acquiring operation. The background and dynamic components are the low-rank and sparse components, respectively. Experimental results demonstrate that the method outperforms an existing technique that minimizes a nuclear-norm of the low-rank component and an L1-norm of the sparse component.

公开(公告)号：US20170024529A1

公开(公告)日：2017-01-26

申请号：US15219484

申请日：2016-07-26

Applicant: Macau University of Science and Technology

Inventor： Yong LIANG ,  Hua CHAI ,  Xiao-Ying LIU

IPC: G06F19/00 ,  G06N99/00 ,  G06F17/18 ,  G06N7/00

CPC classification number: G16H50/20 ,  G06N20/00 ,  G16B10/00 ,  G16B20/00 ,  G16B25/00 ,  G16B40/00 ,  Y02A90/26

Abstract: The present invention provides a novel semi-supervised learning method based on the combination of the Cox model and the accelerated failure time (AFT) model, each of which is regularized with L1/2 regularization for high-dimensional and low sample size biological data. In this semi-supervised learning framework, the Cox model can classify the “low-risk” or a “high-risk” subgroup though samples as many as possible to improve its predictive accuracy. Meanwhile, the AFT model can estimate the censored data in the subgroup, in which the samples have the same molecular genotype. Combined with L1/2 regularization, some genes can be selected by the Cox model and the AFT model and they are significantly relevant with the cancer.

Abstract translation: 本发明提供了一种基于Cox模型和加速故障时间（AFT）模型的组合的新型半监督学习方法，其中每个模型对于高维和低样本量的生物数据进行L1 / 2正则化。 在这个半监督学习框架下，Cox模型可以尽可能多地分类“低风险”或“高风险”亚组，以提高其预测精度。 同时，AFT模型可以估计亚组中的检测数据，其中样本具有相同的分子基因型。 结合L1 / 2正则化，可以通过Cox模型和AFT模型选择一些基因，并且它们与癌症显着相关。