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    METHODS, SYSTEMS, AND MEDIA FOR SEGMENTING IMAGES
    3.
    发明申请
    METHODS, SYSTEMS, AND MEDIA FOR SEGMENTING IMAGES 审中-公开

    公开(公告)号：US20200380695A1

    公开(公告)日：2020-12-03

    申请号：US16885579

    申请日：2020-05-28

    申请人： Zongwei Zhou ,  Md Mahfuzur Rahman Siddiquee ,  Nima Tajbakhsh ,  Jianming Liang

    发明人： Zongwei Zhou ,  Md Mahfuzur Rahman Siddiquee ,  Nima Tajbakhsh ,  Jianming Liang

    IPC分类号： G06T7/143 ,  G06T7/00

    摘要： Methods, systems, and media for segmenting images are provided. In some embodiments, the method comprises: generating an aggregate U-Net comprised of a plurality of U-Nets, wherein each U-Net in the plurality of U-Nets has a different depth, wherein each U-Net is comprised of a plurality of nodes Xi,j, wherein i indicates a down-sampling layer the U-Net, and wherein j indicates a convolution layer of the U-Net; training the aggregate U-Net by: for each training sample in a group of training samples, calculating, for each node in the plurality of nodes Xi,j, a feature map xi,j, wherein xi,j is based on a convolution operation performed on a down-sampling of an output from Xi−1,j when j=0, and wherein xi,j is based on a convolution operation performed on an up-sampling operation of an output from Xi+1,j−1 when j>0; and predicting a segmentation of a test image using the trained aggregate U-Net.

    SYSTEMS, METHODS, AND MEDIA FOR ON-LINE BOOSTING OF A CLASSIFIER
    4.
    发明申请
    SYSTEMS, METHODS, AND MEDIA FOR ON-LINE BOOSTING OF A CLASSIFIER 有权
    用于分类器的在线升压的系统，方法和媒体

    公开(公告)号：US20130070997A1

    公开(公告)日：2013-03-21

    申请号：US13621837

    申请日：2012-09-17

    申请人： Nima Tajbakhsh ,  Hong Wu ,  Wenzhe Xue ,  Jianming Liang

    发明人： Nima Tajbakhsh ,  Hong Wu ,  Wenzhe Xue ,  Jianming Liang

    IPC分类号： G06K9/62

    CPC分类号： G06K9/62

    摘要： Systems, methods, and media for on-line boosting of a classifier are provided, comprising: receiving a training sample; for each of a plurality of features, determining a feature value for the training sample and the feature, using the feature value to update a histogram, and determining a threshold for a classifier of the feature; for each of the plurality of features, classifying the training sample using the threshold for the classifier of the feature and calculating an error associated with the classifier; selecting a plurality of best classifiers from the classifiers; and, for each of the plurality of best classifiers, assigning a voting weight to the one of the plurality of best classifiers.

    摘要翻译： 提供了用于在线升压分类器的系统，方法和介质，包括：接收训练样本; 对于多个特征中的每一个，确定所述训练样本和所述特征的特征值，使用所述特征值来更新直方图，以及确定所述特征的分类器的阈值; 对于所述多个特征中的每一个，使用所述特征的分类器的阈值对所述训练样本进行分类，并计算与所述分类器相关联的错误; 从分类器中选择多个最佳分类器; 并且对于所述多个最佳分类器中的每一个，为所述多个最佳分类器之一分配投票权重。

    METHODS, SYSTEMS, AND MEDIA FOR SELECTING CANDIDATES FOR ANNOTATION FOR USE IN TRAINING CLASSIFIERS
    10.
    发明申请
    METHODS, SYSTEMS, AND MEDIA FOR SELECTING CANDIDATES FOR ANNOTATION FOR USE IN TRAINING CLASSIFIERS 审中-公开

    公开(公告)号：US20190332896A1

    公开(公告)日：2019-10-31

    申请号：US16397990

    申请日：2019-04-29

    申请人： Jianming Liang ,  Zongwei Zhou ,  Jae Shin

    发明人： Jianming Liang ,  Zongwei Zhou ,  Jae Shin

    IPC分类号： G06K9/62

    摘要： Methods, systems, and media for selecting candidates for annotation for use in training classifiers are provided. In some embodiments, the method comprises: identifying, for a trained Convolutional Neural Network (CNN), a group of candidate training samples, wherein each candidate training sample includes a plurality of patches; for each patch of the plurality of patches, determining a plurality of probabilities, each probability being a probability that the patch corresponds to a label of a plurality of labels; identifying a subset of the patches in the plurality of patches; for each patch in the subset of the patches, calculating a metric that indicates a variance of the probabilities assigned to each patch; selecting a subset of the candidate training samples based on the metric; labeling candidate training samples in the subset of the candidate training samples by querying an external source; and re-training the CNN using the labeled candidate training samples.