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    MODEL FOR DETERMINING IHC POSITIVITY
    51.
    发明公开
    MODEL FOR DETERMINING IHC POSITIVITY 审中-公开

    公开(公告)号：US20240355444A1

    公开(公告)日：2024-10-24

    申请号：US18623576

    申请日：2024-04-01

    Applicant: NEC Laboratories America, Inc.

    Inventor： Eric Cosatto

    IPC: G16H20/10 ,  G06T7/00 ,  G06T7/90 ,  G16H50/20

    CPC classification number: G16H20/10 ,  G06T7/0012 ,  G06T7/90 ,  G16H50/20 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06T2207/30096

    Abstract: Methods and systems for diagnosing and treating cancer include performing color deconvolution on an input image, stained according to a second staining process, to generate channels that correspond to dyes used in a first staining process and dyes using in the second staining process. Channels that correlate with a channel used to train a machine learning model are combined to produce a single combined channel. The combined channel is processed using the machine learning model to identify tumor cells. A positivity index is determined based on an output of the machine learning model to aid in medical decision making. A patient's treatment is automatically adjusted based on an output of the machine learning model.

    Anomaly detection with predictive normalization
    53.
    发明授权
    Anomaly detection with predictive normalization 有权

    公开(公告)号：US10964011B2

    公开(公告)日：2021-03-30

    申请号：US16703349

    申请日：2019-12-04

    Applicant: NEC Laboratories America, Inc.

    Inventor： Eric Cosatto ,  Felix Wu ,  Alexandru Niculescu-Mizil

    IPC: G06T7/00 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

    Abstract: A method is provided for model training to detect defective products. The method includes sampling training images of a product to (i) extract image portions therefrom made of a center patch and its context and (ii) black-out the center patch. The method further includes performing unsupervised back-propagation training of a Contextual Auto-Encoder (CAE) model using (i) the image portions with the blacked-out center patch as an input and, (ii) the center patch as a target output and, (iii) an image-based loss function, to obtain a trained CAE model. The method also includes sampling positive and negative center-patch-sized portions from the training images. The method additionally includes normalizing, using the trained CAE model, the positive and negative center-patch-sized portions. The method further includes performing supervised training of a classifier model using the normalized positive and negative center-patch-sized portions to obtain a trained supervised classifier model for detecting the defective products.

    Detecting objects obstructing a driver's view of a road
    57.
    发明授权
    Detecting objects obstructing a driver's view of a road 有权
    检测物体妨碍司机对道路的看法

    公开(公告)号：US09568611B2

    公开(公告)日：2017-02-14

    申请号：US14830873

    申请日：2015-08-20

    Applicant: NEC Laboratories America, Inc.

    Inventor： Eric Cosatto ,  Hans Peter Graf

    IPC: G01S19/51 ,  G01C21/36 ,  B60K35/00 ,  G01S19/48 ,  G01S19/14 ,  G01S19/49 ,  G01S11/12 ,  G01S17/89 ,  G01S17/93 ,  G01S13/93 ,  G01S13/86

    CPC classification number: G01S19/51 ,  B60K35/00 ,  B60K2350/1084 ,  B60K2350/962 ,  B60K2350/965 ,  G01C21/3629 ,  G01C21/3697 ,  G01S11/12 ,  G01S13/867 ,  G01S13/931 ,  G01S17/89 ,  G01S17/936 ,  G01S19/14 ,  G01S19/48 ,  G01S19/49

    Abstract: A system and method for a motorized land vehicle that detects objects obstructing a driver's view of an active road, includes an inertial measurement unit-enabled global position system (GPS/IMU) subsystem for obtaining global position system (GPS) position and heading data of a land vehicle operated by the driver as the vehicle travels along a road, a street map subsystem for obtaining street map data of the GPS position of the vehicle using the GPS position and heading data as the vehicle travels along the road, and a three-dimensional (3D) object detector subsystem for detecting objects ahead of the vehicle and determining a 3D position and 3D size data of each of the detected objects ahead of the vehicle. The street map subsystem merges the street map data, the GPS position and heading data of the vehicle and the 3D position data and 3D size data of the detected objects, to create real-time two-dimensional (2D) top-view map representation of a traffic scene ahead of the vehicle. The street map subsystems finds active roads ahead of the vehicle in the traffic scene, and finds each active road segment of the active roads ahead of the vehicle that is obstructed by one of the detected objects. A driver alert subsystem notifies a driver of the vehicle of each of the active road segments that is obstructed by one of the detected objects.

    Abstract translation: 一种用于检测阻碍驾驶员对主动道路视图的物体的机动陆上车辆的系统和方法，包括：具有惯性测量单元的全球定位系统（GPS / IMU）子系统，用于获得全球定位系统（GPS）的位置和航向数据 当车辆沿着道路行驶时由驾驶员操作的陆地车辆;街道地图子系统，用于当车辆沿着道路行进时，使用GPS位置和航向数据获取车辆的GPS位置的街道地图数据; （3D）物体检测器子系统，用于检测车辆前方的物体，并确定车辆前方的每个检测物体的3D位置和3D尺寸数据。 街道地图子系统将街道地图数据，车辆的GPS位置和航向数据以及检测到的物体的3D位置数据和3D尺寸数据进行合并，以创建实时二维（2D）顶视图地图 车辆前方的交通情况。 街道地图子系统在交通场景中找到车辆前方的活动道路，并且找到被检测到的对象之一阻挡的车辆之前的活动道路的每个活动路段。 驾驶员警报子系统通知被检测对象之一阻塞的每个活动道路段的车辆的驾驶员。

    Corrected Mean-Covariance RBMs and General High-Order Semi-RBMs for Large-Scale Collaborative Filtering and Prediction
    58.
    发明申请
    Corrected Mean-Covariance RBMs and General High-Order Semi-RBMs for Large-Scale Collaborative Filtering and Prediction 审中-公开
    校正平均协方差RBM和一般高阶半RBM用于大规模协同过滤和预测

    公开(公告)号：US20160300134A1

    公开(公告)日：2016-10-13

    申请号：US15088312

    申请日：2016-04-01

    Applicant: NEC Laboratories America, Inc.

    Inventor： Renqiang Min ,  Eric Cosatto

    IPC: G06N3/08 ,  G06N5/04

    CPC classification number: G06N3/0472 ,  G06N3/0445

    Abstract: Systems and methods are disclosed for operating a Restricted Boltzmann Machine (RBM) by determining a corrected energy function of high-order semi-RBMs (hs-RBMs) without self-interaction; performing distributed pre-training of the hs-RBM; adjusting weights of the hs-RBM using contrastive divergence; generating predictions by Gibbs Sampling or by determining conditional probabilities with hidden units integrated out; and generating predictions.

    Abstract translation: 公开了通过在没有自相互作用的情况下确定高阶半RBM（hs-RBM）的校正能量函数来操作限制玻尔兹曼机器（RBM）的系统和方法; 执行hs-RBM的分布式预训练; 使用对比分歧调整hs-RBM的权重; 通过Gibbs Sampling产生预测，或通过确定隐藏的单位来确定条件概率; 并产生预测。

    Computationally Efficient Whole Tissue Classifier for Histology Slides
    59.
    发明申请
    Computationally Efficient Whole Tissue Classifier for Histology Slides 有权
    用于组织学幻灯片的计算有效的全组织分类器

    公开(公告)号：US20140180977A1

    公开(公告)日：2014-06-26

    申请号：US14077400

    申请日：2013-11-12

    Applicant: NEC Laboratories America, Inc.

    Inventor： Eric Cosatto ,  Pierre-Francois Laquerre ,  Christopher Malon ,  Hans-Peter Graf ,  Iain Melvin

    IPC: G06N99/00

    CPC classification number: G06N99/005 ,  G06K9/00147 ,  G06K9/4652 ,  G06K9/623 ,  G06K9/6262 ,  G06K9/6857 ,  G06T7/0012 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/30024

    Abstract: Systems and methods are disclosed for classifying histological tissues or specimens with two phases. In a first phase, the method includes providing off-line training using a processor during which one or more classifiers are trained based on examples, including: finding a split of features into sets of increasing computational cost, assigning a computational cost to each set; training for each set of features a classifier using training examples; training for each classifier, a utility function that scores a usefulness of extracting the next feature set for a given tissue unit using the training examples. In a second phase, the method includes applying the classifiers to an unknown tissue sample with extracting the first set of features for all tissue units; deciding for which tissue unit to extract the next set of features by finding the tissue unit for which a score: S=U−h*C is maximized, where U is a utility function, C is a cost of acquiring the feature and h is a weighting parameter; iterating until a stopping criterion is met or no more feature can be computed; and issuing a tissue-level decision based on a current state.

    Abstract translation: 公开了用于对两个阶段的组织学组织或标本进行分类的系统和方法。 在第一阶段中，该方法包括使用处理器提供离线训练，在该训练期间，基于示例对一个或多个分类器进行训练，包括：将特征分组发现成增加计算成本的集合，为每个集合分配计算成本; 训练每组功能一个分类器使用训练样例; 每个分类器的训练，一个效用函数，其使用训练示例评估为给定组织单位提取下一个特征集的有用性。 在第二阶段中，该方法包括通过提取所有组织单元的第一组特征将分类器应用于未知组织样本; 决定哪个组织单元通过找到最大化分数S = U-h * C的组织单位来提取下一组特征，其中U是效用函数，C是获取特征的成本，h是 加权参数; 迭代直到满足停止标准或不能计算更多的特征; 以及基于当前状态发布组织级决定。

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